Name: Simulation of corn productivity at different sowing times. Simulation of corn productivity at different sowing times, using the AQUACROP model (en Inglés)
SKU: 65451223
Price: 61820.00 CLP
Availability: InStock
Author: Franklin Alves dos Anjos;José Leonaldo de Souza
ISBN: 9786202378925

portada Simulation of corn productivity at different sowing times. Simulation of corn productivity at different sowing times, using the AQUACROP model (en Inglés)

Simulation of corn productivity at different sowing times. Simulation of corn productivity at different sowing times, using the AQUACROP model (en Inglés)

Formato

Libro Físico

Editorial

Our Knowledge Publishing

Autor

Franklin Alves dos Anjos;José Leonaldo de Souza

Año

2025

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22x15 cm

ISBN13

9786202378925

Simulation of corn productivity at different sowing times. Simulation of corn productivity at different sowing times, using the AQUACROP model (en Inglés)

Franklin Alves Dos Anjos;José Leonaldo De Souza (Autor) · Our Knowledge Publishing · Tapa Blanda

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Reseña del libro "Simulation of corn productivity at different sowing times. Simulation of corn productivity at different sowing times, using the AQUACROP model (en Inglés)"

Corn (Zea mays L.), due to its importance in human and animal diets, is one of the most widespread crops in the world. The AquaCrop model was used in this study to simulate total and daily biomass production, grain productivity, and to obtain a corn yield forecast for the Arapiraca region, Alagoas. The model uses canopy cover (CC) instead of leaf area index (LAI) as the basis for separately calculating plant transpiration and soil water evaporation. Yield is calculated as the product of biomass and harvest index (HI). The model input data were from an experiment conducted by Medeiros (2008) in the village of Batingas in the municipality of Arapiraca-AL. For the four sowing seasons, the results of soil water storage simulated by the AquaCrop model showed a similar variation trend to the observed values. This procedure allows for an adequate estimate of grain yield 18 days prior to harvest in the Agreste Alagoano region, enabling end users of the model to plan the storage, logistics, and marketing of the grain crop to be harvested.