https://doi.org/10.29312/remexca.v14i7.3293

elocation-id: e3293

Martínez-Macias, Márquez-Guerrero, Orozco-Vidal, and Reyes-González: Use of remote sensing for the determination of the Kc of the fig crop

Journal Metadata

Journal Identifier: remexca [journal-id-type=publisher-id]

Journal Title Group

Journal Title (Full): Revista mexicana de ciencias agrícolas

Abbreviated Journal Title: Rev. Mex. Cienc. Agríc [abbrev-type=publisher]

ISSN: 2007-0934 [pub-type=ppub]

Publisher

Publisher’s Name: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias

Article Metadata

Article Identifier: 10.29312/remexca.v14i7.3293 [pub-id-type=doi]

Article Grouping Data

Subject Group [subj-group-type=heading]

Subject Grouping Name: Investigation note

Title Group

Article Title: Use of remote sensing for the determination of the Kc of the fig crop

Contributor Group

Contributor [contrib-type=author]

Name of Person [name-style=western]

Surname: Martínez-Macias

Given (First) Names: Karla Janeth

X (cross) Reference [ref-type=aff; rid=aff1]

Superscript: 1

Contributor [contrib-type=author]

Name of Person [name-style=western]

Surname: Márquez-Guerrero

Given (First) Names: Selenne Yuridia

X (cross) Reference [ref-type=aff; rid=aff1]

Superscript: 1

X (cross) Reference [ref-type=corresp; rid=c1]

Superscript: §

Contributor [contrib-type=author]

Name of Person [name-style=western]

Surname: Orozco-Vidal

Given (First) Names: Jorge Arnaldo

X (cross) Reference [ref-type=aff; rid=aff1]

Superscript: 1

Contributor [contrib-type=author]

Name of Person [name-style=western]

Surname: Reyes-González

Given (First) Names: Arturo

X (cross) Reference [ref-type=aff; rid=aff2]

Superscript: 2

Affiliation [id=aff1]

Label (of an Equation, Figure, Reference, etc.): 1

Institution Name: in an Address: División de Estudios de Posgrado-Instituto Tecnológico de Torreón. Torreón, Coahuila, México. CP. 27170. [content-type=original]

Institution Name: in an Address: Instituto Tecnológico de Torreón [content-type=normalized]

Institution Name: in an Address: División de Estudios de Posgrado [content-type=orgdiv1]

Institution Name: in an Address: Instituto Tecnológico de Torreón [content-type=orgname]

Address Line

City: Torreón

State or Province: Coahuila

Postal Code: 27170

Country: in an Address: Mexico [country=MX]

Affiliation [id=aff2]

Label (of an Equation, Figure, Reference, etc.): 2

Institution Name: in an Address: Campo Experimental La Laguna-INIFAP. Blvd. José Santos Valdez Núm. 1200 Pte. Col Centro, Matamoros, Coahuila, México. [content-type=original]

Institution Name: in an Address: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias [content-type=normalized]

Institution Name: in an Address: Campo Experimental La Laguna [content-type=orgdiv1]

Institution Name: in an Address: INIFAP [content-type=orgname]

Address Line

City: Matamoros

State or Province: Coahuila

Country: in an Address: Mexico [country=MX]

Author Note Group

Correspondence Information: [§] Autor para correspondencia: selenne.marquez@hotmail.com. [id=c1]

Publication Date [date-type=pub; publication-format=electronic]

Day: 10

Month: 11

Year: 2023

Publication Date [date-type=collection; publication-format=electronic]

Month: 10

Year: 2023

Volume Number: 14

Issue Number: 7

Electronic Location Identifier: e3293

History: Document History

Date [date-type=received]

Day: 01

Month: 10

Year: 2023

Date [date-type=accepted]

Day: 01

Month: 11

Year: 2023

Permissions

License Information [license-type=open-access; xlink:href=https://creativecommons.org/licenses/by-nc/4.0/; xml:lang=es]

Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons

Abstract

Title: Abstract

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.

Keyword Group [xml:lang=en]

Title: Keywords:

Keyword: Ficus carica L.

Keyword: crop coefficient (Kc)

Keyword: satellite images.

Counts

Figure Count [count=5]

Table Count [count=2]

Equation Count [count=1]

Reference Count [count=18]

Page Count [count=0]

Abstract

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.

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:

NDVI= NIR-RED / NIR+RED

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.

Figure 1

Figure 1. Kc reference of the fig crop.

2007-0934-remexca-14-07-e3293-gf6.jpg

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.

Figure 2

Figure 2. Qualitative NDVI results for 2020 for plot A and plot B.

2007-0934-remexca-14-07-e3293-gf7.jpg

Figure 3

Figure 3. Qualitative NDVI results for 2021 for plot A and plot B.

2007-0934-remexca-14-07-e3293-gf8.jpg

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

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

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

Figure 4

Figure 4. Linear relationship between NDVI and Kc for plots A and B in 2020.

2007-0934-remexca-14-07-e3293-gf9.jpg

Figure 5

Figure 5. Linear relationship between NDVI and Kc for plots A and B in 2021.

2007-0934-remexca-14-07-e3293-gf10.jpg

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.

Conclusions

The present study provides an alternative for calculating Kc remotely; nevertheless, it is recommended to continue monitoring to validate and improve the equation for the fig crop and analyze at least two production cycles.

Bibliography

1 

Aguilar, N.; Galindo-Mendoza, G.; Fortanelli-Martínez, J. y Contreras-Servín, C. 2010. Índice normalizado de vegetación en caña de azúcar en la Huasteca Potosina. Av. Inv. Agrop. 14(2):49-65.

2 

Bautista-Zúñiga, F.; Palacio-Prieto, J. L. y Delfín-González, H. 2011. Técnicas de muestreo para manejadores de recursos naturales. Centro de investigaciones en geografía ambiental. Universidad Nacional Autónoma de México (UNAM). 2da. Ed. 790 p.

3 

Herrera-Puebla, J.; González-Robaina, F. y López-Seijas, T. 2015. Coeficientes de cultivo (Kc) para la determinación de la necesidad de agua en campos deportivos. Rev. Ing. Agríc. 5(2):3-9.

4 

IIRBAVH. 2006. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt. Los Sistemas de Información Geográfica (SIG). Geoenseñanza. 11(1):107-116.

5 

INEGI. 2017. Instituto Nacional de Estadística y Geografía. Cuéntame. Información por entidad. Coahuila. México, DF. https://www.cuentame.inegi.org.mx/monografias/informacion/coah/territorio/clima.aspx?tema=me&e=05.

6 

INTAGRI. 2020. Instituto para la Innovación Tecnológica en la Agricultura. Producción higo en México. Serie frutales. México, DF. 60(1):1-4. https://www.intagri.com/articulos/frutales/produccion-de-higo-en-mexico#.

7 

Krtalic, A.; Prodan, A. and Racetin, I. 2019. Analysis of burned vegetation recovery by means of vegetation indices. International Multidisciplinary Scientific geoConference. https://doi.org/10.5593/sgem2019/2.2/S10.055.

8 

Lizarazo-Salcedo, I. A. y Alfonso-Carbajal, O. A. 2011. Aplicaciones de la agricultura de precisión en palma de aceite ‘Elaeis guineensis’ e hibrido O x G. Rev. Ing. 48(33):124-130.

9 

Márquez-Guerrero, S. Y. 2019. Dinámica nutrimental del cultivo de higo (Ficus carica L.) bajo sistemas de producción intensiva. Tesis de doctorado. Universidad Autónoma de Chapingo (UACH). 77 p.

10 

Olaya, V. F. 2014. Sistemas de Información Geográfica. http://volaya.es/writing. 854 p.

11 

Peraza-Padilla, W.; Rosales-Flores, J.; Esquivel-Hernández, A.; Hilje-Rodríguez, I.; Molina-Bravo, R. y Castillo-Castillo, P. 2013. Identificación morfológica, morfométrica y molecular de Meloidogyne incognita en higuera (Ficus carica L.) en Costa Rica. Agron. Mesoam. 24(2):337-346.

12 

Reyes-González, A.; Kjaersgaard, J.; Trooien, T.; Hay, C. and Ahiablame, L. 2018. Estimation of crop evapotranspiration using satellite remote sensing-based vegetation index. Adv. Meteorol. 1:1-12. DOI: 10.1155/2018/4525021.

13 

Reyes-González, A.; Reta-Sánchez, D. G.; Sánchez-Duarte, J. I.; Rodríguez-Moreno, V. M.; Hernández-Leal, E. y Franco-Gaytán, I. 2020. Desarrollo del coeficiente de cultivo para cártamo forrajero basado en índices de vegetación. Tecnología y Ciencias del Agua. 11(6):39-80. Doi: https://doi.org/10.24850/j-tyca-2020-06-02

14 

Rodríguez-Morán, J. M. y Valdez-Quiles, G. 1999. Riego de la higuera. Comunidad valenciana España. 33-38 pp.

15 

Rosales-Serrano, L. A.; Segura-Castruita, M. Á.; González-Cervantes, G.; Potisek-Talavera, M. D.; Orozco-Vidal, J. A. y Preciado-Rangel, P. 2015. Influencia de los ácidos fúlvicos sobre la estabilidad de agregados y la raíz de melón en casa sombra. Interciencia. 40(5):317-323.

16 

Rueda-Calier, F.; Peñaranda-Mallungo, L. A.; Velásquez-Vargas, W. L. y Díaz-Báez, S. A. 2019. Aplicación de una metodología de análisis de datos obtenidos por percepción remota orientados a la estimación de la productividad de caña para panela al cuantificar el NDVI (índice de vegetación de diferencia normalizada). Corpoica Ciencia y Tecnología Agropecuaria. 16(1):25-40.

17 

Von-Lineo, C. 1753. Ficus carica L. Species Plantarum 2. 1 059 p.

18 

Zamora-Herrera, E.; Duarte-Díaz, C.; Cun-González, R.; Pérez-Hernández, R. y León-Fundora, M. 2014. Coeficientes de cultivo (Kc) en cuba. Rev. Ingen. Agríc. 4(3):16-22.