Cambios en el rendimiento y sus componentes en frijol bajo riego y sequía

Félix, Celia Selene Romero; Castañeda, Cándido López; Shibata, Josué Kohashi; Miranda-Colín, Salvador; Aguilar-Rincón, Víctor Héber; Martínez-Rueda, Carlos Gustavo

doi:10.29312/remexca.v10i2.1607

Cited by 5 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For physiological time, we considered the accumulation of heating degree days since the transplant of the bean plants into the greenhouse (Romero Félix et al, 2019). We conducted multiple regression analyses using the Statistical Analysis System program (SAS, 1999).…”

Section: Methodsmentioning

confidence: 99%

Growth and yield models for black beans under magnetization and pH variation in a greenhouse

Morales-Rivera,

Nava-Cambero,

Antonio-Medina

et al. 2024

View full text Add to dashboard Cite

Objective: To estimate growth and yield variations in common beans (Phaseolus vulgaris L.) treated with a magnetized nutrient solution considering two factors: magnet exposure time and pH level. The significance of this crop lies in its nutritional and economic value. Design/Methodology/Approach: We used a hydroponic system with magnetized Steiner nutrient solution. The design was completely randomized, with a 4 x 6 factorial treatment arrangement and three replications. Factor A comprised exposure times (0.333 hours, 2 hours, chronic, and without magnetization), while Factor B covered solutions with different pH levels (3, 4, 5, 6, 7, and 8). We then applied a multiple regression analysis using the SAS software. Results: Models for vegetative growth variables (plant height, root length, root dry weight, and foliar biomass) and seed yield components (number of pods, number of grains per pod) were statistically significant (p<0.0001). Coefficients of determination ranged from 59.7 % to 82 %, percentages considered appropriate to explain the observed variability. Study limitations/Implications: While the models showed acceptable coefficients of determination, it is essential to consider other factors that were not assessed in this study: exposure to sunlight, insect influence, and diseases that could impact the responses of the bean crop. Findings/Conclusions: Appropriate models to describe vegetative growth and seed yield of the common bean, concerning magnetization time and nutrient solution acidity, include variables such as plant height, root length, root dry weight, foliar biomass, total biomass, number of pods, and number of grains per pod.

show abstract

Section: Methodsmentioning

confidence: 99%

Growth and yield models for black beans under magnetization and pH variation in a greenhouse

Morales-Rivera,

Nava-Cambero,

Antonio-Medina

et al. 2024

View full text Add to dashboard Cite

show abstract

“…This study provides models with predictive sensitivity for IBY-RBY [34] based on extreme essential climate variable events [35][36][37][38]. The results contribute to the prevention of negative effects due to possible decreases in IBY-RBY, helping in the production of sustainable foods [5] in a purely agricultural state, considered to this day as "the breadbasket of Mexico" [20].…”

Section: Introductionmentioning

confidence: 99%

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Atmosphere

View full text Add to dashboard Cite

The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food.

show abstract

“…This study provides models with predictive sensitivity of IBY-RBY [38], based on extreme ECVs events [39][40][41][42][43][44][45][46]. The results contribute to the prevention of negative effects due to possible decreases in IBY-RBY, helping in the production of sustainable foods [5], in a purely agricultural state, considered to this day as "the breadbasket of Mexico" [24].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Yield of Irrigated and Rainfed Bean, Based on Essential Climatic Variables, in the Center and South from Sinaloa State, Mexico

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Preprint

View full text Add to dashboard Cite

The goal was to model the irrigated (IBY) and rainfed (RBY) bean yields, as a function of essential climatic variables (ECVs), in the center (Culiacán) and south (Rosario) from Sinaloa. In Sinaloa, and for the period 1982–2013 (October–March), the following were calculated: a) temperatures. b) average degree days for the bean, c) cumulative potential evapotranspiration and d) cumulative effective precipitation. For ECVs, from the European Space Agency, e) daily soil moisture. f) IBY and RBY, from the Agrifood and Fisheries Information Service. Multiple linear regressions were applied, which modeled IBY–RBY (dependent variables), as a function of ECVs (independent variables). Then, to establish each Pearson correlation (PC) as significantly different from zero, a hypothesis test was applied: PC vs Pearson's critical correlation (CPC). The four models obtained were significantly predictive: IBY–Culiacán (PC = 0.590 > CPC = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|) and RBY–Rosario (PC = 0.532 > CPC = |0.349|). This study is the first in Sinaloa to predict IBY and RBY based on ECVs, contributing to the production of sustainable food.

show abstract

Cambios en el rendimiento y sus componentes en frijol bajo riego y sequía

Cited by 5 publications

References 6 publications

Growth and yield models for black beans under magnetization and pH variation in a greenhouse

Growth and yield models for black beans under magnetization and pH variation in a greenhouse

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

Modeling of Yield of Irrigated and Rainfed Bean, Based on Essential Climatic Variables, in the Center and South from Sinaloa State, Mexico

Contact Info

Product

Resources

About