Sensitivity of Four Indices of Meteorological Drought for Rainfed Maize Yield Prediction in the State of Sinaloa, Mexico

Llanes-Cárdenas Omar, Norzagaray-Campos Mariano, Gaxiola Alberto, Pérez-González Ernestina, Montiel-Montoya Jorge and Troyo-Diéguez Enrique
Additional contact information
Llanes-Cárdenas Omar: Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional, Instituto Politécnico Nacional, (CIIDIR-IPN), Unidad Sinaloa, Jiquilpan 59510, Mexico
Norzagaray-Campos Mariano: Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional, Instituto Politécnico Nacional, (CIIDIR-IPN), Unidad Sinaloa, Jiquilpan 59510, Mexico
Gaxiola Alberto: Facultad de Ingeniería Mochis (UAS-FIM), Universidad Autónoma de Sinaloa, Fuente de Poseidón y Ángel Flores s/n, Los Mochis 81223, Mexico
Pérez-González Ernestina: Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional, Instituto Politécnico Nacional, (CIIDIR-IPN), Unidad Sinaloa, Jiquilpan 59510, Mexico
Montiel-Montoya Jorge: Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional, Instituto Politécnico Nacional, (CIIDIR-IPN), Unidad Sinaloa, Jiquilpan 59510, Mexico
Troyo-Diéguez Enrique: Centro de Investigaciones Biológicas del Noroeste (CIBNOR-La Paz), Av. Instituto Politécnico Nacional 195, Playa Palo de Santa Rita Sur, La Paz 23096, Mexico

Agriculture, 2022, vol. 12, issue 4, 1-14

Abstract: In the state of Sinaloa, rainfall presents considerable irregularities, and the climate is mainly semiarid, which highlights the importance of studying the sensitivity of various indices of meteorological drought. The goal is to evaluate the sensitivity of four indices of meteorological drought from five weather stations in Sinaloa for the prediction of rainfed maize yield. Using DrinC software and data from the period 1982–2013, the following were calculated: the standardized precipitation index ( SPI ), agricultural standardized precipitation index ( aSPI ), reconnaissance drought index ( RDI ) and effective reconnaissance drought index ( eRDI ). The observed rainfed maize yield ( RMY ob ) was obtained online, through free access from the database of the Agrifood and Fisheries Information Service of the government of Mexico. Sensitivities between the drought indices and RMY ob were estimated using Pearson and Spearman correlations. Predictive models of rainfed maize yield ( RMY pr ) were calculated using multiple linear and nonlinear regressions. In the models, aSPI and eRDI with reference periods and time steps of one month (January), two months (December–January) and three months (November–January), were the most sensitive. The correlation coefficients between RMY ob and RMY pr ranged from 0.423 to 0.706, all being significantly different from zero. This study provides new models for the early calculation of RMY pr . Through appropriate planning of the planting–harvesting cycle of dryland maize, substantial socioeconomic damage can be avoided in one of the most important agricultural regions of Mexico.

Keywords: multiple linear regression; multiple nonlinear regression; socioeconomic damage (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/4/525/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/4/525/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:4:p:525-:d:789238

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().