La fertilidad del suelo afecta el crecimiento, nutrición y rendimiento de algodón cultivado en dos sistemas de riego y diferentes dosis de nitrógeno.

Cázares, Jesús Santillano; Díaz, Luis; Núñez-Ramírez, Fidel; Contreras, Rodolfo Faz; Contreras, Fabián Robles; Duarte, Rubén Macías; García, Isabel Escobosa; Salazar, Víctor Cárdenas

doi:10.28940/terra.v37i1.304

Cited by 4 publications

(5 citation statements)

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“…El algodón se encuentra entre los cultivos más explotados, cuyos rendimientos dependen en gran medida del riego eficiente del cultivo, lo cual además de aumentar la productividad, también optimiza la calidad de los productos cosechados, esta práctica agronómica junto con el uso fertilizante resulta ser uno de los factores con mayor efecto sobre el rendimiento del cultivo de algodón (Santillano-Cazares et al, 2019).…”

Section: Requerimientos Nutricionales Del Cultivo De Algodónunclassified

“…La integración de fertilizantes orgánicos en los sistemas de producción de algodón se muestra como una alternativa a los sistemas actuales de producción de algodón a pequeña escala, dado que integra elementos del subsistema de producción animal en el desarrollo de la granja, combinando estiércol de cerdo, estiércol de pollo y biofertilizantes líquidos como alternativas sostenibles que garantizan aportes altos de nitrógeno y fósforo al suelo (Santillano-Cazares et al, 2019).…”

Section: Efectos De La Fertilización Orgánica Sobre El Crecimiento Y ...unclassified

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Efectos De La Fertilización Orgánica en El Cultivo De Algodón (Gossypium Hirsutum)

et al. 2022

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El algodón es una planta muy exigente a nivel nutricional; por ello, es necesario realizar una aplicación eficaz de fertilizantes, destacándose en los últimos tiempos los de origen orgánico. El objetivo de esta investigación fue evaluar, a través de una revisión bibliográfica, la importancia de la fertilización orgánica en la producción sustentable del cultivo de algodón. Se utilizó en el estudio de 44 documentos científicos de las bases de datos Scielo, Redalyc, Latindex y Scopus. La búsqueda de manuscritos científicos comprendió en el período comprendido entre 2002 y 2022, utilizando palabras clave tanto en español como en inglés: ¨algodón¨, ¨origen del algodón¨, ¨distribución del algodón¨, ¨beneficios y usos del cultivo del algodón¨, ¨ciclo vegetativo del algodón¨, ¨fenología del algodón¨, ¨efectos de la fertilización orgánica en el cultivo del algodón¨.¨requerimientos nutricionales del cultivo del algodón¨, ¨fertilización mineral y orgánica del cultivo del algodón¨, ¨efectos de la fertilización orgánica en el crecimiento de la producción de algodón¨. La fertilización orgánica realizada en varios estudios ha consensuado que es posible producir algodón con fertilizantes de origen orgánico (estiércol vacuno, estiércol porcino, guano, gallinaza, humus, etc.). Además, la importancia de la disminución de los costos de producción y la contribución a la mejora de las propiedades físicas del suelo al ayudar a retener la humedad de manera más significativa debido a la alta concentración de materia orgánica.

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Section: Requerimientos Nutricionales Del Cultivo De Algodónunclassified

Section: Efectos De La Fertilización Orgánica Sobre El Crecimiento Y ...unclassified

Efectos De La Fertilización Orgánica en El Cultivo De Algodón (Gossypium Hirsutum)

et al. 2022

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“…Remote sensing data has been employed to accurately assess forest biochemical compositions on broad spatial scales, and various researches developed models to relate reflected spectra to the observed vegetation parameters (Chlus & Townsend 2022;Shen et al 2020). The growth status of plant are linked to soil fertility, and sufficient nutrients support vegetation growth and development (Santillano Cázares et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Mapping soil properties in tropical rainforest area using UAV-based hyperspectral images and LiDAR points

Chen,

Shi,

et al. 2024

Preprint

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Aims This study aimed to integrate UAV-based hyperspectral images and LiDAR points to indirectly estimate the soil nutrient properties in tropical rainforest areas. Methods A total of 175 features, including vegetation indices, texture characteristics, and forest parameters, were extracted from the study area. Five machine learning models, Partial Least Squares Regression (PLSR), Random Forest (RF), AdaBoost, Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost), were constructed to predict soil nutrients. Furthermore, Bayesian optimization algorithm (BOA) was introduced to obtain more optimal model hyperparameters. Results The results showed that BOA can better explain the complex interactions between features and hyperparameters, leading to an average improvement of model performance by 89.38% compared to default parameter models. The GBDT model optimized by BOA outperformed other models in predicting soil pH and TN, with improvements of 512.50% and 36.36%, respectively. The XGBoost model with optimized parameters performed well in predicting SOC and TP (with gains of 206.67% and 95% improvements, respectively). In addition, point cloud features derived from LiDAR data outperformed vegetation indices in predicting soil nutrient properties, enhancing inversion accuracy by effectively characterizing vegetation growth conditions and terrain changes. Conclusions This study indicated that combining the advantages of UAV-based hyperspectral images and LiDAR points will advance the methodology for digitally mapping soil nutrient properties in forested areas, achieving large-scale soil nutrient management and monitoring.

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“…Wei et al [ 9 ] have shown that the NO 3 − –N content in the last four leaf petioles of cotton during the bud stage is significantly correlated with yield, which can sensitively indicate the N nutrition status of cotton and serve as a recommended diagnostic period for cotton topdressing.The accuracy of cotton yield prediction is directly related to the fertilization level during important growth periods [ 19 ]. Therefore, reliable prediction methods [ 18 ] can help excessive fertilizer application, reduce environmental pollution and unnecessary costs [ 20 ]. With the rapid rise of artificial intelligence, neural network technology [ 21 ] provides new methods and approaches for agricultural applications.…”

Section: Introductionmentioning

confidence: 99%

Establishment of an NPK nutrient monitor system in yield-graded cotton petioles under drip irrigation

Dong,

Liu,

et al. 2023

Plant Methods

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Background The determination of nutrient content in the petiole is one of the important methods for achieving cotton fertilization management. The establishment of a monitoring system for the nutrient content of cotton petioles during important growth periods under drip irrigation is of great significance for achieving precise fertilization and environmental protection. Methods A total of 100 cotton fields with an annual yield of 4500–7500 kg/ha were selected among the main cotton-growing areas of Northern Xinjiang. The nitrate nitrogen (NO3−–N), inorganic phosphorus (PO43−–P) and inorganic potassium (K+–K) content and yield of cotton petioles were recorded. Based on a yield of 6000 kg/ha as the dividing line, a two-level and yield-graded monitoring system for NO3−–N, PO43−–P and K+–K in cotton petioles during important growth periods was established, and predictive yield models for NO3−–N, PO43−–P and K+–K in petioles during important growth periods were established. Results The results showed found that the yields of the 100 cotton fields surveyed were normally distributed. Therefore, two yield grades were classified using 6000 kg/ha as a criterion. Under different yield-graded, the NO3−–N, PO43−–P and K+–K content of petiole at important growth stages was significantly positively correlated with yield. Further, the variation range of NO3−–N, PO43−–P and K+–K content in petioles could be used as a standard for yield-graded. In addition, a yield prediction model for the NO3−–N, PO43−–P and K+–K content of petioles was developed. The SSO-BP validation model performed the best (R2 = 0.96, RMSE = 0.06 t/ha, MAE = 0.05 t/ha) in the full bud stage, which was 12.9% higher than the BP validation model. However, the RMSE and MAE were decreased by 86.7% and 88.1%, respectively. Conclusion The establishment of NPK nutrition monitor system of cotton petioles under drip irrigation based on yield-graded provides an important basis for nutrition monitor of cotton petiole under drip irrigation in Xinjiang. It also provides a new method for cotton yield prediction.

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La fertilidad del suelo afecta el crecimiento, nutrición y rendimiento de algodón cultivado en dos sistemas de riego y diferentes dosis de nitrógeno.

Cited by 4 publications

References 15 publications

Efectos De La Fertilización Orgánica en El Cultivo De Algodón (Gossypium Hirsutum)

Efectos De La Fertilización Orgánica en El Cultivo De Algodón (Gossypium Hirsutum)

Mapping soil properties in tropical rainforest area using UAV-based hyperspectral images and LiDAR points

Establishment of an NPK nutrient monitor system in yield-graded cotton petioles under drip irrigation

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