Estimativa de produção de laranja valência pela adaptação de um modelo agrometeorológico

Abstract

YIELD PREDICTION OF ORANGE CULTIVAR VALENCIA THROUGH ADAPTATION OF AN AGROMETEOROLOGICAL MODELThe influence of the hydric factor influence and the previous year yield relative to the potential (Yaa/Yp), yield was evaluated for orange cultivar Valencia/Cravo (Citrus sinensis Osbeck). The stages of dormancy, flowering and fruit set were considered in a multiplicative mathematic agrometeorological model with different sensitive indices (λ-values). Meteorological, phenological and crop yield data of an orchard in Matão, SP, Brazil, were analyzed. The obtained results indicated better performance of the model when the periods of dormancy were established in August -September, flowering in OctoberNovember and fruit set in December -January. The magnitude of the sensitive index values showed that flowering (Oct -Nov) is the most sensitive period for water stress. In the model validation it was observed a good performance, with R 2 of 0.81 and d-index of agreement of 0.95.

Do Gridded Weather Datasets Provide High-Quality Data for Agroclimatic Research in Citrus Production in Brazil?

“…Agrometeorological models, on the other hand, tend to incorporate this knowledge in the equations that constitute the model [18][19][20]23,26]. This allows for better predictions in scenarios with a small volume of data but tends to finetune the model for a specific crop, variety, area, and weather pattern [27].…”

“…This allows for better predictions in scenarios with a small volume of data but tends to finetune the model for a specific crop, variety, area, and weather pattern [27]. Nevertheless, using agrometeorological models based on sequential equations is the traditional approach for citrus yield prediction [18][19][20]23,26]. Therefore, this approach will be used in this work.…”

“…Despite similar objectives, five of the most important yield prediction models utilized in sweet oranges show different outputs, encompassing yield, fruit volume, fruit quality, and water productivity (Table 1). Camargo et al [26] and Martins and Ortolani [18] adapted the Jensen [30] model for the 'Valencia' sweet orange. However, with the use of precipitation and temperature data to calculate potential (PET) and actual evapotranspiration (AET), the calculation method decided by the authors penalized the yield according to the water conditions in critical phenological stages of the crop.…”

“…In this work, our main objective was to identify the gaps in the literature related to the evaluation of the climatic data inputs from those three main datasets (INMET, NasaPower, and BR-DWGD) in order to predict sweet orange yield in different regions of the Brazilian citrus belt. For this purpose, a widely used agrometeorological model for sweet orange yield prediction was considered [18], with the following scenarios: (i) using data from the NasaPower database; (ii) using data from the INMET database; and (iii) using data from the BR-DWGD database.…”

Previsão do número de frutos a partir da quantidade de estruturas reprodutivas em laranjeiras

“…Nestes modelos mecanísticos, o mote é relacionar as causas e os efeitos na relação das plantas com o ambiente; entretanto, essa modelagem é mais complexa e requer maior número de dados. No entanto, poucos trabalhos são encontrados na literatura utilizando a modelagem dinâmica em variedades de laranjeiras nacionais (Camargo et al, 1999;Volpe et al, 2002;Martins & Ortolani, 2006) Assim, a modelagem para a estimativa da fenometria e produtividade torna-se complexa na tentativa de se levar em conta os fatores ambientais que influenciam na cultura. Entretanto, simplificadamente, pode-se considerar a própria planta como integralizadora das condições ambientais, sendo que o crescimento, desenvolvimento, produtividade e qualidade dos frutos são decorrentes da integração com o ambiente (Reuther, 1973).…”