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Revista mexicana de ciencias agrícolas
versión impresa ISSN 2007-0934
Rev. Mex. Cienc. Agríc vol.14 no.7 Texcoco sep./nov. 2023 Epub 08-Dic-2023
https://doi.org/10.29312/remexca.v14i7.3293
Articles
Use of remote sensing for the determination of the Kc of the fig crop
1División de Estudios de Posgrado-Instituto Tecnológico de Torreón. Torreón, Coahuila, México. CP. 27170.
2Campo Experimental La Laguna-INIFAP. Blvd. José Santos Valdez Núm. 1200 Pte. Col Centro, Matamoros, Coahuila, México.
The fig crop has been introduced as a potential fruit alternative in our country. Nevertheless, there is little information regarding the efficient management of the fig crop, such as the crop coefficient, which is of great importance for the programming of efficient irrigation; through irrigation sheets that provide the crop with the water requirement for its production. Through satellite images, the normalized difference vegetation index, and the reference crop coefficient, an equation of the crop coefficient was determined for the fig crop with the power to provide technical information for irrigation programs.
Keywords: Ficus carica L.; crop coefficient (Kc); satellite images.
El cultivo de higo se ha introducido como una alternativa frutícola potencial en nuestro país. Sin embargo, existe poca información referente al manejo eficiente del cultivo de higo, como el coeficiente del cultivo, el cuál es de gran importancia para la programación del riego eficiente; a través, de láminas de riego que proporcionen al cultivo el requerimiento de agua para su producción. A través, de imágenes de satélite, el índice de vegetación de diferencia normalizada y el coeficiente del cultivo de referencia, se determinó una ecuación de coeficiente del cultivo para el cultivo de higo con facultad de aportar información técnica para los programas de riego.
Palabras clave: Ficus carica L.; coeficiente de cultivo (Kc); imágenes de satélite.
The complexity of current environmental problems, such as climate change, deforestation, soil degradation, water scarcity, among others, demands the use of geospatial techniques and tools such as geographic information systems (GIS) (Bautista et al., 2011), which are a set of software and hardware designed to read, edit, store, manage and analyze data, which are represented through maps, graphs, reports, etc. (Olaya, 2014).
GIS have become an important tool for researchers and professionals who require the management of databases related to various levels of spatial or territorial aggregation (IIRBAVH, 2006), such as the agricultural sector. Precision agriculture integrates various spatial information technologies by recognizing, locating, quantifying, and recording spatial and temporal variability of an agricultural unit, optimizing crop productivity, improving agronomic decisions of different crops for each specific site (Lizarazo-Salcedo and Alfonso-Carbajal, 2011).
The normalized difference vegetation index (NDVI) is a model universally used for the monitoring of vegetation in different environments, it uses a combination of reflectance values with high sensitivity in vegetation changes and low sensitivity to other types of information.
The NDVI is obtained from the ratio of red and near-infrared reflectance, reflected by the vegetation captured by the satellite sensor; values range from +1 to -1. Values of 0.1 correspond to rocky, sandy, or snowy areas, those from 0.2 to 0.3 correspond to areas with shrubs, meadows, or natural pastures, while values greater than 0.6 indicate temperate or tropical forests; that is, the highest values are for denser vegetation and with greater photosynthetic activity, while lower values for little vegetation, so it is considered an estimator of the amount of biomass and water needs of crops (Aguilar et al., 2010; Reyes-González et al., 2018; Rueda-Calier et al., 2019).
To estimate the water needs of a specific crop, the crop coefficient (Kc) and the reference evapotranspiration are used, which is why this method helps to plan and manage when and how much water should be used for irrigation prospectively, projectively, and in real-time (Zamora-Herrera et al., 2014; Herrera-Puebla et al., 2015). The fig tree is native to the Mediterranean region, is drought tolerant, supports high levels of salts, and adapts to infertile soils, offering advantages in its production in arid and semi-arid regions (Von Linneo, 1753; Peraza-Padilla et al., 2013).
The fig has a high nutritive, nutraceutical, and antioxidant capacity and can be consumed fresh, dehydrated, and minimally processed (INTAGRI, 2020). According to data from (FAOSTAT, 2020), in 2018, world fig production exceeded one million tons, where Turkey was the largest producer, with 270 000 t, while Mexico produced 7 000 t year-1, in an area of 1 340 ha (Márquez-Guerrero et al., 2019), with Veracruz being the state with the highest yields per hectare (INTAGRI, 2020).
Due to its versatility, the fig crop has been introduced as a sustainable fruit alternative in arid and semi-arid areas, hence its importance in La Comarca Lagunera. However, there is no up-to-date information on the intensive management of the fig crop, such as Kc estimation, evapotranspiration (ET), and irrigation treatments. It is of interest to generate technical information that supports the sustainable management of the crop with the help of geographic information systems.
Experimentation site
The fieldwork was carried out in the ejido El Vergel, in Gómez Palacio, Durango (25° 39’ 16.679” north latitude, -103° 30’ 4.129”). With climate Bw (h’) hw (e), which corresponds to a hot desert dry climate with a rainfall regime in summer and extreme oscillation (Rosales-Serrano et al., 2015), with temperature ranges ranging from 4 °C to more than 30 °C and average annual precipitation of 250 mm concentrated from June to September (INEGI, 2017).
Satellite images
Images from the Sentinel-2 satellite with a processing level 1C were downloaded from the United States Department of Agriculture (USDA) website, from January 2020 to November 2021. The images were taken to a 2nd processing level, with an atmospheric correction DOS1, the QGIS 3.20 software.
NDVI Calculation
For the calculation of the NDVI, the formula described by Krtalic et al. (2019) was used:
Where: NIR= near infrared. RED= red. For the Sentinel-2 satellite, these represent the bands B8 for near-infrared and B4 for red.
Kc calculation
The calculation of Kc was carried out according to the methodology by Reyes-González et al. (2020), where the NDVI is plotted against the reference Kc of the crop, obtaining an equation that determines the real Kc of the crop based on the NDVI. The reference Kc (Figure 1) was taken from the article by Rodríguez and Valdez (1999), where they estimate the water needs of the fig tree during the years 1996, 1997, and 1998, obtaining the monthly Kc of the crop.
The reference Kc agrees with the phenological stages of the fig crop reported by Márquez-Guerrero (2019), which are: November (11), December (12), January (1): latency stage. February (2): pruning. March (3), April (4): vegetative growth. May (5): fruit development and breba harvest. June (6): fruit ripening. July (7), August (8): high fruit harvest. September (9): medium fruit harvest. October (10): postharvest.
Analysis of information
With the ArcMap 10.3 software, the NDVI estimate was made for the years 2020 (Figure 2) and 2021 (Figure 3), considering two plots cultivated with fig, one plot A with 14 ha and drip irrigation, and one plot B with 25 ha and gravity irrigation.
Considering the uniformity of each plot, A and B, represented in Figures 2 and 3, 15 points were taken at random and averaged to obtain an NDVI value (Table 1 and 2) and plotted (Figures 4a-b, 5a-b) against the reference Kc as suggested by Reyes-González et al. (2020).
Table 1 Average NDVI for plots A and B of 2020.
| Date | Julian day | Reference Kc | NDVI small plot | NDVI large plot |
|---|---|---|---|---|
| January 18 | 18 | 0.14 | 0.22 | 0.2 |
| February 12 | 43 | 0.15 | 0.21 | 0.17 |
| March 13 | 73 | 0.16 | 0.26 | 0.25 |
| April 12 | 103 | 0.45 | 0.44 | 0.45 |
| May 17 | 138 | 0.6 | 0.58 | 0.43 |
| June 06 | 158 | 0.63 | 0.54 | 0.39 |
| July 16 | 198 | 0.65 | 0.72 | 0.54 |
| September 09 | 253 | 0.65 | 0.52 | 0.53 |
| October 19 | 293 | 0.5 | 0.54 | 0.41 |
| November 18 | 323 | 0.22 | 0.44 | 0.28 |
| December 18 | 353 | 0.14 | 0.35 | 0.3 |
Table 2 Average NDVI for plots A and B of 2021.
| Date | Julian day | Reference Kc | NDVI small plot | NDVI large plot |
|---|---|---|---|---|
| January 18 | 18 | 0.14 | 0.26 | 0.25 |
| February 12 | 43 | 0.15 | 0.44 | 0.29 |
| March 13 | 73 | 0.16 | 0.29 | 0.22 |
| April 12 | 103 | 0.45 | 0.35 | 0.25 |
| May 17 | 138 | 0.6 | 0.46 | 0.3 |
| June 06 | 158 | 0.63 | 0.52 | 0.33 |
| July 16 | 198 | 0.65 | 0.59 | 0.45 |
| August 10 | 222 | 0.65 | 0.71 | 0.53 |
| September 19 | 262 | 0.65 | 0.64 | 0.39 |
| October 14 | 287 | 0.5 | 0.59 | 0.43 |
| November 13 | 317 | 0.22 | 0.48 | 0.3 |
In Figure 4, the most representative R2 value was obtained in the large plot for 2020, so the proposed equation to determine the Kc of the fig crop would be KC= 0.5111NDVI+0.1586. For the year 2021 (Figure 5), equations with low linear correlation were obtained, this due to the late agronomic management of the crop in the orchard, such as pruning at the wrong time, inadequate fertilization, poor pest control, caused by the Covid-19 health contingency.
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Received: October 01, 2023; Accepted: November 01, 2023
Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
