The cultivation of bananas (Musa spp.) is a basic
source of food worldwide (Ghag & Ganapathi,
2017), as well as an important agricultural export product for many
tropical countries, including Ecuador, where its commercialization in the
international market is one of the country's most significant economic
activities (Evans et al., 2020) Annual banana
production increases by an average of 1.37 tons per hectare (Varma & Bebber, 2019), which is closely related
to the increase in productivity, which is achieved through efficient,
technician management that takes into account both the physiological needs of
the crop and the soil and climatic conditions of the area in which it is grown (Panigrahi et al., 2021).
A good agronomic
management of crops can be achieved with the delimitation of agricultural
management units or agricultural management zones (MZs), which allow the
optimization of resources; economic, social and environmental (X. Song et al., 2009). The importance of a
differentiated management is due to the variability of the physical and
chemical attributes of the soil, but also to irrigation, since in the banana
crop, if managed incorrectly, it can generate losses in fruit mass of up to 65%
(Panigrahi et al., 2021) or an increase in the
incidence of insect pests and diseases, especially Black Sigatoka (Mycosphaerella
fijiensis Morelet) (Yonow et al., 2019) In
the province of El Oro, a large part of the irrigation systems are controlled
by a manual operator, who without specific guidance causes by omission water
wastage and inaccuracy in irrigation control, affecting crop productivity (Berrú-Ayala et al., 2020; Panigrahi et al., 2021).
As a measure to mitigate
these errors, precision agriculture has facilitated the automation of several
processes in irrigation management with which the costs for the execution of
procedures are reduced (Vélez et al., 2024),
such as the delimitation of MZs with which the state of the vegetation and the
variation present within a crop can be identified from spatial information
obtained from satellite images, from which the yield or health of the
vegetation can be estimated (HARRIS et al., 2005;
Kureel et al., 2022) This information can be associated to the water
conditions of the crop with which the water management of the crop can be
focused (Erazo-Mesa et al., 2024; Gorokhova &
Pankova, 2024). Additionally, it is important to consider that the water
needs must be in function of the soil, climate and plant relationship, which
will allow to increase the efficiency of irrigation management (Blum, 2011; Zinkernagel et al., 2020).
With the delimitation of
the MZs, the technicians responsible for field management can make correct
decisions (Sawadogo et al., 2023), considering
the spatial variability of the physical and chemical attributes of the soil (Villegas Santa & Castañeda Sánchez, 2020),
which are related to different physical properties of the soil such as:
porosity, bulk density, texture and structure that influence the water
retention capacity and availability of water for the crop (Coelho et al., 2019; Ding et al., 2020).
The literature reports
delimitations of MZs using satellite images obtained from remote sensors as
instruments to obtain information from an object at a distance in a manned or
unmanned means of transport (MIAO et al., 2018)
have been contrasted with MZs generated from physical (Igor
et al., 2020), chemical (Ayele et al., 2019),
hydric (Priori et al., 2020) and biological (Priori et al., 2019) properties, demonstrating
their efficiency for the delimitation of MZs. The processing of remotely sensed
information has been tested in several crops, including banana (Gomez Selvaraj et al., 2020; Wikantika et al., 2022),
allowing the identification of plants by means of aerial imagery and machine
learning methods and the identification of vegetation health (Cui et al., 2023; Wu et al., 2024). Precision
agriculture and spectral indices obtained from satellite images make it
possible to identify areas with lower productivity on a farm or to estimate
crop yields using the normalized difference vegetation index (NDVI), the
soil-adjusted vegetation index (SAVI) (Chlingaryan et
al., 2018; El-Hendawy et al., 2019) to estimate soil moisture at field
capacity and permanent wilting point using the water stress index (MSI) (Welikhe et al., 2017).
With the considerations
described above, the general objective of this research is delimiting
agricultural management zones (MZs), considering the spatial variability of
soil physical and chemical attributes and the information of spectral indices
calculated from satellite image data and the soil-climate-plant relationship to
increase the reliability of the information.
our images were selected
using a KLM and the EarthExplorer interface, and a Shapefile was included to
delimit the farm in the download. For image processing, the image was cropped
with a mask layer on the farm and the resolution between bands was adjusted to
10 meters. Normalized land cover index (NDVI), normalized difference adjusted
index (SAVI) and MSI water stress index (Cohen et al., 2013) were selected using the following
equations:
NDVI=((NIR-RED))/((NIR+RED))
where: NDVI is the
Normalized Difference Vegetation Index; NIR the near-near infrared region; RED
the red region. In Sentinel 2 the NIR corresponds to band 8 (spectral
resolution of 0.78 - 0.90 μm) and RED to band 4
(spectral resolution of 0.65 - 0.68 μm).
The Soil Adjusted
Vegetation Index (SAVI) has a slight variation of the traditional NDVI formula
to avoid distortions in the analysis values when vegetation is located on
exposed soils (Rhyma et
al., 2020). Conditions such as temperature or humidity can
influence the working bands analyzed and thus the results provided by the
indicator. In this case, the SAVI vegetation index will try to avoid this
influence of the soil on the results by adding an additional factor (L) in the
NDVI equation that will allow working in scenarios where vegetation development
is incipient.
SAVI=((NIR-RED))/((NIR+RED+L))*(1+L)
Water Stress Index (MSI)
for canopy water deficit analysis, productivity prediction and biophysical
modelling (Welikhe et
al., 2017). The interpre-tation of the MSI is reversed relative
to other aquatic vegetation indices; thus, higher values of the index indicate
higher plant water stress and, in inference, lower soil moisture content.
Values of this index range from 0 to more than 3, with the common range for
green vegetation being 0.2 to 2.
MSI=MidIR/NIR
To avoid errors due to
overlapping satellite images in the study area, spectral index data were
filtered for each situation assessed. Values in a 10 m band around the
perimeter of the area and the areas where fruit processing and housing takes
place, especially at the edges of the area that were close to other
plantations, were excluded. This procedure allowed for the normalization of
NDVI and SAVI data, as well as productivity data, and allowed for an accurate
comparison between these parameters. This methodology was used to ensure the
accuracy and reliability of the results (Damian et al., 2020).
Delimitation of
management zones using sate-llite images
To define the management
zones, a combination of three spectral indices (NDVI, SAVI and MSI) obtained
from the average of four nearby satellite images was used. The average values
of each index were processed using the Smart-map plug-in of QGIS version
3.22.11 (Pereira
et al., 2022). First, data including the coordinates extracted
from the image and the index values were loaded. Then, the management zones
window was reviewed, the data were interpolated, and the number of classes
generated was identified. In this case, three zones with several iterations
equal to 500 were adjusted according to the FPI and NCE values. This metho-dology
was applied following the methodology described in the document (Balafoutis et al., 2017).
Delimitation of
Management Zones by physical-chemical properties
The management zones were
determined using the soil characteristics provided by the company PACIDEL S.A.
For this purpose, the farm was delimited by means of a planimetric survey and
sampling points were generated within each hectare. ArcGIS image processing
tools, including kriging-based interpolation models and Voronoi polygon
division, were used to create the management zones (Villaseñor et al., 2021).
Soil characteristics
and adjustment of water requirements
To determine the
irrigation needs of the banana crop, the CROPWAT software was used, based on
the crop requirements (30). These calculations were carried out to compare
previous water applications, as well as to adjust them according to the
climatology of the area and the physical and chemical properties of the soil.
To gather information on climatology, the meteorological yearbooks of the
nearest meteorological station of the National Institute of Meteorology and
Hydrology (INAMHI), with station code M0292 (Luna-Romero et al., 2018) were consulted (Table 1).
This station is located at an altitude of 5 m above sea level, at latitude
3.17°S and longitude 79.54°W.
As for the calculation of
the effective precipitation of the crop using the FAO/AGLW method, where the
effective precipitation is calculated by the formula 0.6*P -10/3 when the
monthly preci-pitation is less than or equal to 70/3 mm and varies from 0.6 to
0.8*P-24/3 when the monthly precipi-tation is greater than 70/3 mm implemented
in the same CROPWAT software (Villazón Gómez et al., 2021) the irrigation
programming in the charac-teristics of the crop was carried out using the data
obtained from the FAO manual 56 (Valle Júnior et al., 2021) due to the fact that it is
a perennial crop the irrigation programming of the crop was carried out by
calculating the phase of the crop at 365 days, Kc (crop coefficient) of the
crop 1.10 root depth of the crop of 0.70 m, critical exhaustion level 0.65,
yield response 1.35 and crop height of 3 m, because the soil characteristics
are variants on the farm was determined to perform the calculations of the
sheet time and frequency of irrigation through the Excel software package
Office, based on this background is performed pro-average calculation of
irrigation needs per month.
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