Equationing Arabica coffee: Adaptation, calibration, and application of an agrometeorological model for yield estimation

IF 6.1 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Cleverson Henrique de Freitas , Rubens Duarte Coelho , Jéfferson de Oliveira Costa , Paulo Cesar Sentelhas
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引用次数: 0

Abstract

CONTEXT

Coffee cultivation is important to Brazil's economy, positioning the country as a global leader in production and export. Given the complex environmental and management factors affecting yields, particularly due to climate change, there is a pressing need from farmers and dealers for more precise crop estimation models.

OBJECTIVE

This study aimed to refine and calibrate an agrometeorological model, originally developed by Santos and Camargo (2006) and later adapted by Verhage et al. (2017a), to estimate Arabica coffee yield in the main producing regions of Minas Gerais and São Paulo. Additionally, sensitivity analysis was also performed to identify the most influential model parameters and variables.

METHODS

Yield data from 28 coffee-producing locations (2003−2020) and meteorological data alongside irrigation use were employed. Following calibration and adaptation, a sensitivity analysis was conducted to determine the model's response to variations in coffee plant parameters and environmental conditions. Local sensitivity analysis (LSA) focused on meteorological variables, while global sensitivity analysis (GSA) addressed coffee-related parameters.

RESULTS AND CONCLUSIONS

The adaptations proposed to the original model led to a significant refinement in the yield estimates, emphasizing the complex interactions between climatic variables and agricultural management practices. Key adaptations include the estimation of potential yield (Yp), the incorporation of temporal curves for root growth, leaf area index, available water capacity, and crop coefficient, as well as a water balance that accounts for irrigation and its effect on attenuating high canopy temperatures. Calibration improved the model's accuracy and precision, with the RMSE decreasing from 13.66 (819.6 kg ha−1; 1 bag ha−1 = 60 kg ha−1) to 8.65 (519.0 kg ha−1) bags ha−1, R2 improving from 0.62 to 0.65, d-index from 0.79 to 0.88, and NSE from 0.09 to 0.64. During the evaluation phase, with independent data, RMSE was 7.76 bags ha−1 (465.6 kg ha−1), d-index 0.85, and R2 0.55. Sensitivity analysis emphasized the importance of mean temperature and solar radiation on Yp, as well as the impact of irrigation practices and water deficit management under rainfed conditions. Additionally, factors specific to the coffee plant itself directly affect its yield.

SIGNIFICANCE

The findings underscore the importance of a multifactorial and adaptive approach to coffee cultivation, addressing the complexities and challenges posed by varying climatic conditions. This work offers valuable insights into optimizing coffee production, presenting the model as a tool for developing more resilient cultivation strategies and enhancing the sustainability of Brazilian Arabica coffee in future climate change scenarios.

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来源期刊
Agricultural Systems
Agricultural Systems 农林科学-农业综合
CiteScore
13.30
自引率
7.60%
发文量
174
审稿时长
30 days
期刊介绍: Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments. The scope includes the development and application of systems analysis methodologies in the following areas: Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making; The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment; Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems; Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.
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