Datasets Horticulturae 2023. Predictive Model
Agriculture is the main driver of depletion resources worldwide, and its duty is to ensure food security within a rapidly increasing demographic and urbanization, so it is important to transi-tion to sustainable production systems. Vertical crops (VCs) can reduce the pressure on conven-tional agricu...
| Main Authors: | , , , , , |
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| Format: | info:eu-repo/semantics/report |
| Language: | Spanish / Castilian |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/14861 |
| _version_ | 1789406264771477504 |
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| author | Guzmán Palomino, José Miguel López Mora, Manuel Felipe Quintero Castellanos, María Fernanda Salas Sanjuan, María Del Carmen González Murillo, Carlos Alberto Borgovan, Calina Borgovan, Calina |
| author_facet | Guzmán Palomino, José Miguel López Mora, Manuel Felipe Quintero Castellanos, María Fernanda Salas Sanjuan, María Del Carmen González Murillo, Carlos Alberto Borgovan, Calina Borgovan, Calina |
| author_sort | Guzmán Palomino, José Miguel |
| collection | DSpace |
| description | Agriculture is the main driver of depletion resources worldwide, and its duty is to ensure food security within a rapidly increasing demographic and urbanization, so it is important to transi-tion to sustainable production systems. Vertical crops (VCs) can reduce the pressure on conven-tional agriculture because they save water and nutrients and increase crop yield. Therefore, this study aimed to validate a proposed predictive model (PM) to simulate water and nutrient uptake in vertical crops under greenhouse conditions. Based on the Penman-Monteith equation, PM es-timates transpiration, while nutrient uptake was estimated using the Carmassi-Sonneveld sub-model. PM was experimentally evaluated for vertically grown lettuce under Mediterranean greenhouse conditions, during spring 2023. The irrigation technique was a closed-loop fertiga-tion circuit. The experimental consisted of testing two densities (50 and 80 plants·m-2), where each unit of the experiment unit was divided into three heights (lower, medium, and upper). It performed ANOVA with a value of p < 0.05 and R2 to assess PM performance. The results sug-gest a high degree of PM, since R2 ranged from 0.6 to 0.8 for the uptake of water and nutrients. Both densities had a yield between 17-20 times higher than conventional lettuce production and significant savings in water, between 85-88%. In this sense, PM has great potential to intelli-gently manage VC fertigation, saving water and nutrients, which represents an advance towards reaching SDG 6 and SDG 12, within the 2030 |
| format | info:eu-repo/semantics/report |
| id | oai:repositorio.ual.es:10835-14861 |
| institution | Universidad de Cuenca |
| language | Spanish / Castilian |
| publishDate | 2023 |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-148612023-12-21T09:16:12Z Datasets Horticulturae 2023. Predictive Model Predictive Model to Evaluate Water and Nutrient Uptake in Vertically Grown Lettuce under Mediterranean Greenhouse Conditions Guzmán Palomino, José Miguel López Mora, Manuel Felipe Quintero Castellanos, María Fernanda Salas Sanjuan, María Del Carmen González Murillo, Carlos Alberto Borgovan, Calina Borgovan, Calina Agronomía Model Agriculture is the main driver of depletion resources worldwide, and its duty is to ensure food security within a rapidly increasing demographic and urbanization, so it is important to transi-tion to sustainable production systems. Vertical crops (VCs) can reduce the pressure on conven-tional agriculture because they save water and nutrients and increase crop yield. Therefore, this study aimed to validate a proposed predictive model (PM) to simulate water and nutrient uptake in vertical crops under greenhouse conditions. Based on the Penman-Monteith equation, PM es-timates transpiration, while nutrient uptake was estimated using the Carmassi-Sonneveld sub-model. PM was experimentally evaluated for vertically grown lettuce under Mediterranean greenhouse conditions, during spring 2023. The irrigation technique was a closed-loop fertiga-tion circuit. The experimental consisted of testing two densities (50 and 80 plants·m-2), where each unit of the experiment unit was divided into three heights (lower, medium, and upper). It performed ANOVA with a value of p < 0.05 and R2 to assess PM performance. The results sug-gest a high degree of PM, since R2 ranged from 0.6 to 0.8 for the uptake of water and nutrients. Both densities had a yield between 17-20 times higher than conventional lettuce production and significant savings in water, between 85-88%. In this sense, PM has great potential to intelli-gently manage VC fertigation, saving water and nutrients, which represents an advance towards reaching SDG 6 and SDG 12, within the 2030 2023-12-21T09:16:11Z 2023-12-21T09:16:11Z 2023-12-19 info:eu-repo/semantics/report http://hdl.handle.net/10835/14861 es Horticulturae 2023, 9, x. https://doi.org/10.3390/xxxxx CPP 2021-008801 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
| spellingShingle | Agronomía Model Guzmán Palomino, José Miguel López Mora, Manuel Felipe Quintero Castellanos, María Fernanda Salas Sanjuan, María Del Carmen González Murillo, Carlos Alberto Borgovan, Calina Borgovan, Calina Datasets Horticulturae 2023. Predictive Model |
| title | Datasets Horticulturae 2023. Predictive Model |
| title_full | Datasets Horticulturae 2023. Predictive Model |
| title_fullStr | Datasets Horticulturae 2023. Predictive Model |
| title_full_unstemmed | Datasets Horticulturae 2023. Predictive Model |
| title_short | Datasets Horticulturae 2023. Predictive Model |
| title_sort | datasets horticulturae 2023. predictive model |
| topic | Agronomía Model |
| url | http://hdl.handle.net/10835/14861 |
| work_keys_str_mv | AT guzmanpalominojosemiguel datasetshorticulturae2023predictivemodel AT lopezmoramanuelfelipe datasetshorticulturae2023predictivemodel AT quinterocastellanosmariafernanda datasetshorticulturae2023predictivemodel AT salassanjuanmariadelcarmen datasetshorticulturae2023predictivemodel AT gonzalezmurillocarlosalberto datasetshorticulturae2023predictivemodel AT borgovancalina datasetshorticulturae2023predictivemodel AT borgovancalina datasetshorticulturae2023predictivemodel AT guzmanpalominojosemiguel predictivemodeltoevaluatewaterandnutrientuptakeinverticallygrownlettuceundermediterraneangreenhouseconditions AT lopezmoramanuelfelipe predictivemodeltoevaluatewaterandnutrientuptakeinverticallygrownlettuceundermediterraneangreenhouseconditions AT quinterocastellanosmariafernanda predictivemodeltoevaluatewaterandnutrientuptakeinverticallygrownlettuceundermediterraneangreenhouseconditions AT salassanjuanmariadelcarmen predictivemodeltoevaluatewaterandnutrientuptakeinverticallygrownlettuceundermediterraneangreenhouseconditions AT gonzalezmurillocarlosalberto predictivemodeltoevaluatewaterandnutrientuptakeinverticallygrownlettuceundermediterraneangreenhouseconditions AT borgovancalina predictivemodeltoevaluatewaterandnutrientuptakeinverticallygrownlettuceundermediterraneangreenhouseconditions AT borgovancalina predictivemodeltoevaluatewaterandnutrientuptakeinverticallygrownlettuceundermediterraneangreenhouseconditions |