© The Authors, 2023, Published by the Universidad del Zulia*Corresponding author: fmem1963@gmail.com
Keywords:
GIS
Drone
Smart and precision agriculture
Fertilization
Geographic information system and unmanned aerial vehicles for soil and pastures evaluation
Sistemas de información geográcos y vehículos aéreos no tripulados para la evaluación de suelos
y pastos
Sistemas de informação geográca e veículos aéreos não tripulados para avaliação de solos e
pastagens
University of Zulia, Faculty of Agronomy
Bolivarian Republic of Venezuela
Abstract
The objective of this review is to present soil and pasture evaluation
studies using georeferenced aerial photographs captured by sensors on board
drones and analyzed using the Geographic Information System (GIS) to
develop strategies for use in the management of pastures and farm potential.
The use of intensive grazing systems requires advanced knowledge for
ecient management, and smart and precision agriculture represents a
strategy to reduce costs. Using GIS and drones, an immediate comprehensive
diagnosis is obtained, such as quantication of the degradation of a pasture
or farm, distribution of botanical composition, and variability of soil and
pasture nutrients to generate fertilization plans by specic area (precision
agriculture).
Freddy Espinoza
1
*
Victor Sevilla
2
Diego Machado
3
1
Instituto
Nacional
de
Investigaciones
Agrícolas
(INIA).
Centro
Nacional
de
Investigaciones
Agropecuarias
(CENIAP), Venezuela.
2
Universidad Central de Venezuela, Facultad de Agronomía,
Venezuela.
3
Sistema Integrado de Apoyo al Productor (SIAP), Venezuela.
Received: 18-09-2023
Accepted:
24-10-2023
Published:
28-11-2023
Rev. Fac. Agron. (LUZ). 2023, 40(Supplemet): e2340Spl03
ISSN 2477-9407
DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v40.n4.supl.03
Crop Production
Associate editor: Dra. Evelyn Peréz-Peréz
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2023, 40 (Supplement): e2340Spl03. October-December. ISSN 2477-9407.2-6 |
Resumen
La presente revisión tiene como objetivo mostrar estudios
de evaluación de suelos y pastos, utilizando fotografías aéreas
georreferenciadas capturadas por sensores a bordo de drones y
analizadas mediante el sistema de información geográco (SIG),
para desarrollar estrategias de uso en el manejo de los pastos y el
potencial de la nca. Hoy día con el uso de sistemas intensivos de
pastoreo, se requieren conocimientos avanzados para un manejo
eciente, donde la agricultura inteligente y de precisión, representen
una estrategia para disminuir costos. A través de los SIG y drones
se puede obtener un diagnóstico integral inmediato, tales como:
cuanticación de la degradación de la pastura o nca, distribución
de la composición botánica, variabilidad de los nutrientes del suelo
y de la pastura. Además, se generan planes de fertilización por área
especíca (agricultura de precisión), entre otros.
Palabras clave: SIG, dron, agricultura inteligente y de precisión,
fertilización.
Resumo
O objetivo desta revisão é mostrar estudos de avaliação de solos e
pastagens, utilizando fotograas aéreas georreferenciada capturadas
por sensores a bordo de drones e analisadas por meio do Sistema de
Informações Geográcas (SIG) para desenvolver estratégias de uso
no manejo de pastagens e o potencial da fazenda. Hoje, a utilização de
sistemas intensivos de pastoreio requer conhecimento avançado para
um manejo eciente, onde a agricultura inteligente e a de precisão
representam uma estratégia para redução de custos. Por meio de
SIG e drones, se obtem um diagnóstico abrangente imediato, como:
quanticação da degradação de pastagens ou fazendas, distribuição de
composição botânica, variabilidade de solo e nutrientes de pastagens
para gerar planos de fertilização por área especíca (agricultura de
precisão), entre outros.
Palavras-chave: GIS, drone, agricultura inteligente e de precisão,
fertilização.
Introduction
Nowadays, the use of intensive grazing systems, either the
so-called Voissin grazing, ultra-dense grazing, cellular grazing,
strip grazing, among others, require advanced knowledge for their
ecient management. For this, it is necessary to know the amount
of nutrients extracted by animals and plants, in order to replenish
them in time and avoid the weeding of paddocks and the degradation
of pastures and soils. In addition, Animal Production Systems
have advanced considerably. In this sense, the use of techniques to
improve Reproduction, Nutrition, Genetics and Health have led to
great advances in obtaining more productive animals. However, for
animals to respond to these improvement techniques, it is necessary
to provide them with good quality pasture and forage that will last
over time.
Ocial gures from 1950 to 2007 indicated that the pasture area
in Venezuela had regressed for 57 years, with pasture degradation
being one of the reasons for this delay, as a consequence of
inadequate management (Espinoza et al., 2012). On the other hand,
average production costs in 2022, for the liter of milk at the feedlot
gate, have ranged between 0.32 and 0.46 $.kg
-1
and the standing
meat between 1.27 and 1.66 $.kg
-1
(Instituto Venezolano de la Leche
y la Carne [Invelecar], 2022; Observatorio Lácteo [OCLac], 2022).
These costs make us rethink the economic eciency of the system
and implement techniques to be protable and sustainable, where
pastures play a decisive role. Therefore, today it is essential to focus
on precision agriculture or livestock farming, using remote sensors
on board unmanned drones, supported by global positioning systems
(GPS) and spatial analysis using Geographic Information Systems
(GIS) technology, which would allow determining the best strategy
to optimize the use of available resources.
GIS are used in precision agriculture to collect, store and analyze
data in order to increase crop eciency through the understanding and
variation observed in soils, their association with crops, yields and the
use of fertilization practices, which are of interest for decision making
(Cantos et al., 2022). In Venezuela, they have been used mostly in
cartographic and cadastral studies, watershed characterizations and
in certain agricultural crops (Alberdi and Erba, 2022; Cabeza et al.,
2021; Figueredo et al., 2018; Olivares et al., 2021; Rey-Brina et al.,
2020; Sevilla et al., 2009), but there are no works cited in pasture
evaluation.
The objective of this article was to show soil and pasture
evaluation studies, using georeferenced aerial photographs, captured
by drone-borne sensors, and analyzed with GIS techniques to develop
strategies for pasture management use and farm potential.
Methods
Maps and data, product of unpublished eld reports, developed in
Venezuela and Costa Rica, between 2017 and 2021, were used. In this
sense, two farms were diagnosed in Venezuela, located in the states
of Bolivar (Agropecuaria J & M, C.A., UTM coordinates 623215 E
and 823321 N) and Tachira (La Coromoto, 806899 E and 917100 N);
and a third farm in Heredia, Costa Rica (Espinos, UTM 815515 E and
1112271 N).
Each farm was geographically located and climatic and edaphic
data, relief, hydrology, vegetation cover variability and the use of
satellite images were recorded. In addition, historical data and digitized,
geo-referenced and vectorized plans were obtained, which were
superimposed on the geospatial information subsequently collected
on the farm lands, in order to previously select the points geolocated
by geographic coordinates for soil and pasture sampling. Then, in the
eld, photographic scenes were taken by ying with a quadcopter
drone equipped with a sensor (CMOS of ½.3’’) and eective pixels
of 12.4 M, with a ight altitude between 80 and 120 meters above
sea level and longitudinal and transversal overlap of 80%. To do
so, the following steps were followed: Denition of the study area,
generation of ight plans (height, time and ight lines, longitudinal
and transversal overlap of the photographic scenes), positioning and
GPS measurement of ground control points, calibration of the drone
(compass, GPS and camera), ight and taking aerial photographs.
This procedure allowed, through photogrammetric processes, the
obtaining of ducial points of each photographic scene with east and
north coordinates, the orientation of the aerial shot and the design of
the point cloud to generate an orthophotomap of the farm, as well as
a digital model of the farm’s surface, at detailed spatial resolutions
of 5 to 10 cm. To achieve this procedure and the products generated,
computer applications were used, such as: GNSS Solution Version
10.1, MapSource and Sokkia Link Version 2.0 (for post processing
of ground control points that support the cartographic accuracy of
the orthophotomap), Arc-Gis Version 10.1 (for information overlay
and spatial analysis) and Agisoft Version 1.2.5 (for photogrammetric
processing of the point cloud and orthophotomap).
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Espinoza et al. Rev. Fac. Agron. (LUZ). 2023, 40 (Supplement): e2340Spl03
3-6 |
Results and discussion
Figure 1 shows two maps of physical-chemical properties of the
soils of a 50 ha farm, located in the south of Venezuela in Bolivar
State, where it is evident that the soils are decient in calcium in more
than 85 % of the farm (red shades), sucient in a very low proportion
in the north (green shades), but with critical levels in the northwest
(yellow color); while zinc was decient only in a part of the eastern
part of the farm. According to the concept of precision agriculture, this
indicated that the application of the micro element would be only on
the basis of that sector, which represents 15% of the surface, while Ca
should be applied almost in its totality. Pinargote and Pacheco (2021)
considered that precision agriculture, in addition to participating in
the sustainability and protability of the agricultural system, brings
great benets by reducing the environmental impact of agriculture.
This aspect is important, considering that the application of high
levels of agrochemicals is considered today as an environmental
problem in the carbon footprint, even more so in livestock systems.
1a. Distribución espacial del contenido de calcio en el suelo (meq/100g) en la unidad de
producción Agropecuaria J & M: Spatial distribution map of soil exchangeable calcium in
the farm J&M. Leyenda: Legend. Lotes: Lots. Puntos de muestreo de suelo: Soil sampling
points. meq/100 g: meq.100 g
-1
. Parámetro: Parameter. Niveles críticos: Critical levels. Calcio
(meq/100 g): Calcium (meq.100 g
-1)
. Muy bajo: Very low. Bajo: Low. Medio: Medium. Alto:
High. Muy alto: Very high.
1b. Distribución espacial del contenido de Zinc en el suelo (ppm) en la unidad de producción
Agropecuaria J & M: Spatial distribution of zinc content in agricultural soils of the J&M farm.
Leyenda: Legend. Lotes: Lots. Puntos de muestreo de suelo: Soil sampling points. Parámetro:
Parameter. Niveles críticos: Critical levels. Zinc (ppm): Zinc (ppm). Muy bajo: Very low. Bajo:
Low. Medio: Medium. Alto: High. Muy alto: Very high.
Figure 1. Maps of soil properties at the J & M Agropecuaria
farm, El Callao, Bolivar State, Venezuela. 1a) Calcium
distribution. 1b) Zinc distribution.
Geolocated sampling of soil and plant tissue (foliar sampling)
Using GPS, the points previously geo-referenced for the evaluation
of soils and grasses were located in the eld. For the soil study, a
general characterization of the environment was made, highlighting
climatic, geological, physiographic, vegetation and current use
aspects, such as: precipitation, temperature, slope, stoniness, micro-
relief, cover and agricultural use. Then, the soil prole was described
to a depth of one meter, detailing elements such as textural class,
color, structure, hardness, friability, drainage, presence of roots and
biological activity. Finally, soil samples were taken at each geo-
referenced point for subsequent laboratory analysis of physical-
chemical properties (texture, CEC, MO, macro and micronutrients).
In the case of pastures, the Relative Importance Value (RIV)
method described by Espinoza et al. (2000) was used, which reects
the percentage ratio of the species present in the pastures. The total
biomass production was also estimated and samples were taken to
determine the chemical composition (macro and microelements).
Homogeneous management units for suitability and potential
of the farm
In order to standardize the areas, homogeneous management units
were delimited based on a cluster analysis of the dierent soil property
maps, the digital surface model, the slope map and the description of
the soil prole at the sampling sites. For this purpose, greater weight
was given to the delimitation of the units to physical properties, such
as slope and the distribution of clays and sands, because they are more
stable over time. In this sense, for the evaluation of land suitability
for the sustainability of the farm, the methodology of the Food and
Agriculture Organization of the United Nations ([FAO], 1997) was
used, for the classication for fertility purposes, the methodology of
Sánchez et al. (1982) and for the classication of land according to its
capacity for agricultural use, the system proposed by Comerma and
Arias (1971).
Predictive maps of soil and pasture nutritive value studies
With the information from samples sent to the soil and pasture
laboratories, continuous maps of physicochemical properties were
generated, using mathematical interpolation and geostatistical
models, such as spline, topogrid and cokriging (Castro et al., 2017),
which allowed to adequately adjust the levels of natural fertility and
nutritive content of pastures.
Pasture health index
This work raises the possibility of using, not replacing, estimated
spectral indices, from the visible bands place index signicance
(RGB), as an alternative, given the diculty of using traditional
indices such as place index signicance (NDVI), given the high cost
of multispectral sensors; from the orthophotomap of images obtained
by RGB sensors on board drones. Then, using map algebra, the
normalized green red dierence index (NGRDI) was calculated, which
is linearly related to the normalized dierence of the reectances of
both bands, using equation 1 (Gitelson, et al., 2002).
NGRDI = (DN green – DN red) / (DN green + DN red) (Eq. 1)
This index allowed inferring the state of health of the pastures,
through the nutritional state of the vegetation, ground cover
density, pest and disease attack. For the calibration, a statistical and
geostatistical analysis was carried out between the value of the index
and the bands with the measurement of the variables, both in the
laboratory and in the eld, in order to adjust the limits of the equation
with respect to the classes obtained in the eld.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2023, 40 (Supplement): e2340Spl03. October-December. ISSN 2477-9407.4-6 |
The following is the morphological evaluation and position of the
landscape at Agropecuaria J & M, El Callao, Bolivar state, Venezuela
(gure 2).
2a. Pedregosidad de los suelos Agropecuaria J & M: Surface stoniness of the soils of the J
& M farm. Leyenda: Legend. Clases: Classes. Baja: Low. Media: Medium. Alta: High. Área
(ha): Area (ha).
2b. Posición en el paisaje: Position in the landscape. Leyenda: Legend. Clases: Classes. Baja:
Low. Media: Medium. Alta: High. Área (ha): Area (ha).
Figure 2. Morphological evaluation and position of the landscape
at Agropecuaria J & M, El Callao, Bolivar State,
Venezuela. 2a) Stoniness. 2b) Landscape.
Based on the characterization data (gure 2) and the results of the
physical-chemical analysis of the soils, and using the technique of
homogeneous management units for suitability and potential of the
farm, the possible pasture and forage species to be established in the
production unit were recommended, generating an adaptation map by
sector (gure 3).
Predictive maps allow visualizing the spatial distribution of the
nutrient content of pasture tissue, such is the case of a study carried
out in Costa Rica, at the beginning of the dry period and the beginning
of the rainy season in a dairy and equine farm (gure 4).
Figure 4 shows adequate sulfur values in the dairy cow and heifer
modules with star grass (Cynodon plectostachyus), located to the
north and west of the farm; while in the equine sector to the east,
critical and decient levels of this nutrient are observed in transvala
grass (Digitaria decumbens). The Figure clearly shows that the
sulfur content in the aforementioned pastures is decreasing from the
northwest and southwest to the west, reaching decient levels in the
hybrid transvala grass (red color). This information can be important
for the knowledge of nutritionists and the elaboration of diets for
precision cattle breeding. However, for greater precision it is likely
that a greater number of samples would be required, but also the costs
of the study would rise. Barnetson, et al. (2020), stated that when
remote sensors are used they are not exempt from sampling errors,
suggesting further research in this regard.
Zonicación forrajera: Forage zoning.
Figure 3. Species adaptable to the Agropecuaria J & M farm, El
Callao, Bolivar State, Venezuela.
Distribución espacial del contenido foliar de azufre (%) en potreros de la nca Espinos:
Spatial distribution of foliar sulfur content (%) in paddocks of the Espinos farm. Punto de
muestreo de tejido: Tissue sampling spot. Perimetral de la nca: Farm perimeter. Potreros:
Paddocks.
Figure 4. Spatial distribution of sulfur content in star grass and
switchgrass tissue on a Costa Rican farm. Evaluation
April 2021.
One of the aspects to consider in pasture management is to
accurately determine ooded and upland areas. The digital elevation
model (DEM) is a tool to separate these areas and dene aspects
of pasture management. DEMs are geomatic products that allow
characterizing the area, such as terrain elevation, surface, slope,
aspect, curvature and zones of inuence (Mena et al., 2011).
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Espinoza et al. Rev. Fac. Agron. (LUZ). 2023, 40 (Supplement): e2340Spl03
5-6 |
Figure 5 shows the DEM of the “La Coromoto” farm over an
area of 170 ha, where the physiographic units of banco and bajío are
separated, in addition to presenting the proportion of the Urochloa
radicans species. The study also showed a high proportion of weeds
(37.5%).
% Pasto Tanner: Tanner grass %. Fisiografía: Physiography. Bajío: Lowland. Banco: Bank.
Muestreo de suelos: Soil sampling.
Figure 5. Physiography and distribution of Tanner Grass
(Urochloa radicans) at La Coromoto farm, Táchira
State, Venezuela.
This aspect, together with the observation of tatucos (gure 6)
and the health index (gure 7), suggest management practices to be
recommended, among which are the use of harrowing, fertilization
and animal load adjustments, among others. Tatucos are the product
of reticular erosion, and are very common in the 500,000 hectares of
southern Lake of Maracaibo (Comerma, 2009).
Mena et al. (2007), consider that the data processed by GIS
determine homogeneous sectors, allowing an integrated analysis of
spatially expressed territorial variables. Based on the methodologies
for land classication and suitability, after dening the homogeneous
management units, a detailed orthophotomap was obtained (gure 8).
General recommendations were also obtained through a menu of
options (tables 1 and 2), where 13 management units were classied
according to the intrinsic characteristics of the farm (bank and bajio
units, classied according to soil acidity and fertility, classied
as Typic Hapludalfs (bancos) and Aeric Epiaquepts (bajios),
recommending a fertilization and pasture management plan.
Figure 6. Drone view of the area where the presence of tatucos in
the paddocks can be seen.
Índice de salud: Health index. Pobre-Sin vegetación: Poor-No vegetation. Moderada:
Moderate. Buena: Good. Muy buena: Very good.
Figure 7. State of health of paddocks in La Coromoto farm,
Táchira state, Venezuela.
Mapa de unidades de manejo Finca “La Coromoto”: Map of management units "La Coromoto"
farm. Leyenda: Legend. Puntos de muestreo de suelo: Soil sampling points. Perimetral:
Perimeter. Potreros: Paddocks. Fisiografía del terreno: Physiography of the terrain. Unidades
de manejo: Management units. Banco con alta fertilidad: Bank with high fertility. Banco con
moderada fertilidad: Bank with moderate fertility. Banco con baja fertilidad: Bank with low
fertility. Banco con muy baja fertilidad: Bank with very low fertility. Bajío con alta fertilidad:
Lowlands with high fertility. Bajío con moderada fertilidad: Lowland with moderate fertility.
Bajío con baja fertilidad: Lowland with low fertility. Bajío con muy baja fertilidad: Lowland
with very low fertility.
Figure 8. Homogeneous management units in La Coromoto farm,
Táchira, Venezuela.
Conclusions
Geographic information systems and unmanned aerial vehicles are
a valuable tool for the evaluation of pasture and agri-food resources,
generating a rapid diagnosis of the entire farm and management
plans, making it possible to propose strategic fertilization by specic
area (precision agriculture). Using the methodology employed with
GIS, it is feasible to recommend potential maps to improve livestock
production units, through a menu of options.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Espinoza et al. Rev. Fac. Agron. (LUZ). 2023, 40 (Supplement): e2340Spl03
6-6 |
Table 1. Menu of options to strengthen the La Coromoto Production Unit, Táchira State, Venezuela.
Units System Option Crop Use Description Paddock Observations
BN-MF-SA
Pastoralism
and Agriculture
Pasture for Grazing
Agricultural Crops
Legume
Decumbens
Ruziziensis
Legume
Tanner
Guinea-grass
Passion fruit
Grazing
Hay
Protein in association
Self-consumption
and sale
Establishment of new
pastures by over-seeding
Follow fertilization
recommendations
1-2-3-52-53-
54-55-62-63-
68-69
Maintain the species
present in the paddock.
BJ-LF-EA Pastoril Pasture for grazing Tanner Grazing Follow fertilization plan 26-27-37
Handling animal loads
and days of rest and
occupation.
BN: Bancos; BJ: Bajios, MF: Moderate fertility, LF: Low fertility, SA: Strong acidity, EA: Extreme acidity (Not published).
Table 2. Fertilization levels (kg.ha
-1
.yr
-1
) in the dierent physiographic units. La Coromoto Farm, Táchira State.
Fertilizante BN-HF BN-MF BN-LF BN-MB BJ-HF BJ-MF BJ-LF BJ-VLF
Phosfate rock from Riecito 350 350 400 400 200 300 350 350
Phosfate rock from Monte Fresco 400 400 450 450 250 400 400 400
Pearl urea (U) 0 0 0 0 250 250 250 250
Potasium chloride (KCl) 25 0 25 0 50 0 50 50
BN: Bancos; BJ: Bajios, HF: High fertility, MF: Moderate fertility, LF: Low fertility, VLF: Very low fertility
With the use of GIS and drones it is possible to obtain quantiable
products such as: the quantication of the degree of degradation
of a pasture or farm, the distribution of botanical composition,
the determination of the variability of soil and pasture nutrients,
the separation of physiographic areas through the MDE to dene
management plans, such as irrigation and the adequate management
of forage species.
Acknowledgement
The authors would like to thank the company Sistema Integrado
de Apoyo al Productor (SIAP) for the studies carried out.
Literature cited
Alberdi, R., & Erba, D. (2022). Introducción a los Sistemas de Información
Geográca (SIG) aplicados al catastro. Editorial Universidad Católica
de Santa Fe. https://www.perlego.com/book/3547803/introduccin-a-los-
sistemas-de-informacin-geogrca-sig-aplicados-al-catastro-pdf
Barnetson, J., Phinn, S., & Scarth, P. (2020). Estimating plant pasture
biomass and quality from UAV imaging across Queensland´s
rangelands. AgriEngineering, 2(4), 523-543. https://doi.org/10.3390/
agriengineering2040035
Cabeza, M., Henao, A., & Manrique, J. (2021). Aplicación de SIG para la
jerarquización de sitios de relleno sanitarios. Área Metropolitana de
Mérida, Venezuela. Revista Forestal Latinoamericana, 29(55), 49-87.
http://www.saber.ula.ve/bitstream/handle/123456789/47237/art3.pdf?
Cantos, E., Inga, J., Macías, D., & Martínez, T. (2022). Los sistemas de
información geográca aplicados a la agricultura de precisión. Revista
Cientíca Arbitrada Multidisciplinaria PENTACIENCIAS, 4 (3), 62-76.
https://editorialalema.org/index.php/pentaciencias/article/view/131/183
Castro, M., García, D., & Jiménez, A. (2017). Comparación de técnicas de
interpolación espacial de propiedades del suelo en el piedemonte llanero
colombiano. Revista Tecnura, 21(53), 78-95. https://www.redalyc.org/
journal/2570/257054721006/html/
Comerma, J. (2009). Suelos mal drenados en Venezuela. Agronomía Tropical,
59(1), 25-32. http://sian.inia.gob.ve/revistas_ci/Agronomia%20Tropical/
at5901/pdf/comerma_j.pdf
Comerma, J., and Arias, L. (1971). Un sistema para evaluar las capacidades de
uso agropecuarios de los terrenos en Venezuela [Presentación de paper].
Seminario de Clasicación Interpretativo con Fines Agropecuarios,
Maracay, Venezuela. https://edepot.wur.nl/485573
Espinoza, F., Días, Y., Valle, A., Perdomo, E., León, L., Vilorio, R., & Roye, F.
(2000). Utilización del banco de energía como estrategia de manejo en
sabanas del estado Cojedes. I. Composición botánica. Zootecnia Tropical,
18(2), 197-212.
Espinoza, F., Gil, J., & Chacón, E. (2012). Situación de las pasturas en Venezuela
y su relación con la carga animal. Revista Alcance 72, Facultad de
Agronomía UCV, 41-56. http://saber.ucv.ve/ojs/index.php/rev_agro/
article/view/15236
Organización de las Naciones Unidas para la Agricultura y la Alimentación [FAO].
(1997). Zonicación agro-ecológica. Guía general. Boletín de suelos de la
FAO 73. https://www.fao.org/3/W2962S/w2962s00.htm
Figueredo, L., Rey, J., Andrade, O., & Quintero, J. (2018). Análisis geoestadístico
de poblaciones de huevos de Aenolamia varia (Hemiptera:cercopidae) en
caña de azúcar. Revista Academia, 17(39), 31-41. http://www.saber.ula.
ve/handle/123456789/44641
Gillan, J., McClaran, M., Swetnam, & Heilman P. (2019). Estimating forage
utilization with drone based photogrammetric point clouds. Rangeland
Ecology & Management, 72(4), 575-585. https://doi.org/10.1016/j.
rama.2019.02.009
Gitelson, A., Kaufman, Y., Stark, R., & Rundquist, D. (2002). Novel algorithms for
remote estimation of vegetation fraction. Remote Sensing Environment,
80 (1), 76-87. http://dx.doi.org/10.1016/S0034-4257(01)00289-9
Instituto Venezolano de la Leche y la Carne. (2022). Estadísticas Mercado
Nacional. http://invelecar.org/
Mena, C., Molina, L., Ormazábal, Y., & Morales, Y. (2011). Generalización de
modelo digital de elevación condicionada por puntos críticos de terreno.
Boletim de Ciências Geodésicas, 17 (3), 439-457. https://www.redalyc.
org/pdf/3939/393937721007.pdf
Mena, C., Ormazábal, Y., Llanos, J., & Díaz, J. (2007). Desarrollo de un Sistema
de Información Geográco para Mejorar la Gestión del agua de Riego del
Embalse Convento Viejo, Chile. Agricultura Técnica (Chile), 67(1), 49-
59. https://www.bioline.org.br/pdf?at07006
Observatorio Lácteo. (2022). Precios de la leche cruda y el queso a puerta de
corral. https://www.observatoriolacteo.org/estadisticas/
Olivares, B., Rey, J., Lobo, D., Navas-Cortés, J., Gómez, J., & Landa, B. (2021).
Fusarium Wilt of Bananas: A review of agro-environmental factors in
the venezuelan production system aecting its development. Agronomy,
11(5), 986. https://doi.org/10.3390/agronomy11050986
Pinargote, C., and Pacheco, H. (2021). Discriminación de malezas basada en la
respuesta espectral del cultivo de maíz, Manabí, Ecuador. Revista de la
Facultad de Agronomía (LUZ), 38(4), 785-805. https://doi.org/10.47280/
RevFacAgron(LUZ).v38.n4.03
Rey-Brina, J., Martínez-Solorzano, G., Ramírez, H., & Pargas-Pichardo, R. (2020).
Marchitez del Banano Cavendish, y su relación con las condiciones
agroecológicas en una planicie lacustrina de Venezuela. Agronomía
Tropical, 70, 1-12. https://doi.org/10.5281/zenodo.4346252
Sánchez, P., Couto, W., & Buol, S. (1982). El sistema de clasicar suelos de
acuerdo con su fertilidad, interpretación, aplicaciones y modicaciones.
Geoderma, 27(4), 283-309. https://doi.org/10.1016/0016-7061(82)90019-2
Sevilla, V., Comerma, J., & Silva, O. (2009). Caracterización de la cuenca del río
Canoabo en el estado Carabobo, Venezuela. III. La erosión de los suelos.
Agronomía Tropical, 59(3), 249-264. https://dialnet.unirioja.es/servlet/
articulo?codigo=5226498
