Evidence map›Paper›PMID 41507777›Full record

ArticleBMC plant biology2026

Construction of growth model and growth simulation of Yunnan Arabica coffee.

Rongbiao Ji, Yehui Wu, Yadong Li, Zongyuan Lv, Mengyao Wu, Li Chen, Jianping Yang

Abstract read
In one paragraph

Article in BMC plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Rongbiao Ji *College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China.
Yehui Wu *Yuxi Agriculture Vocation-Technical College, Yuxi, Yunnan, 653106, China.
Yadong LiCollege of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China.
Zongyuan LvCollege of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China.
Mengyao WuCollege of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China.
Li ChenCollege of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China.
Jianping YangCollege of Big Data, Yunnan Agricultural University, Kunming, Yunnan, 650201, China. yangjp@ynau.edu.cn.

Funding

Major Project of Yunnan Provincial Science and Technology No. 202502AE090019Yunnan Provincial Science and Technology Talent and Platform Program (Academician Expert Workstation) No. 202405AF140013
6 · The paper itself

Abstract

Yunnan is the primary coffee-producing region in China. With the changing consumption habits of younger consumers, the coffee industry has experienced rapid growth, and enhancing both quality and yield has become a major focus of research. However, crop growth models are predominantly applied to the simulation of annual grain crops. To date, there has been no reported research on the development of growth models specifically for Arabica coffee in Yunnan.This study is based on the coffee growth model initially assessed and calibrated using long-term experimental sites in Costa Rica and Nicaragua in Central America (Ovalle-Rivera O. et al. Agrofor. Syst. 94:2033-2051, 2020), and has been locally calibrated to construct a growth model for Arabica coffee in Yunnan. The main tasks are as follows: Focusing on the key coffee-growing regions in Yunnan, data on local climate, soil, phenology, management practices, and yield were collected. The study explored the calibration of model parameters using Bayesian optimization, gradient descent, and particle swarm optimization algorithms. By comparing two indicators-root mean square error (RMSE) and mean absolute error (MAE)-it was determined that particle swarm optimization produced the best results under Yunnan's ecological and climatic conditions, and thus, this algorithm was used to construct the Yunnan Arabica coffee growth model. Furthermore, simulations were conducted to examine the effects of varying fertilization amounts, shading practices, shading intensities, altitudes, and weather conditions on coffee growth. The study discusses the impact of these factors on the growth and yield of Yunnan Arabica coffee, providing scientific evidence for optimizing cultivation management strategies. The results indicate that moderate fertilization and shading can significantly improve coffee yield, although both factors have a "saturation point." Within a specific range of total fertilizer application, the impact of single application rates on final yield is not significant. This suggests that when formulating a scientifically sound fertilization strategy, the total nitrogen fertilizer application should be optimized first. Among the major production areas, the climate conditions in the Pu'er region are the most ideal, while other regions require tailored management measures. In summary, this research successfully constructed the Yunnan Arabica coffee growth model and simulated its performance under various conditions, revealing potential response trends of coffee to environmental changes. It provides theoretical support for the cultivation management and sustainable development of the Arabica coffee industry in Yunnan.

Indexed as

CoffeaModels, BiologicalChinaClimateCAF2014CoffeeCrop growth modelParameter calibrationSimulation

Identifiers

PMID41507777
PMCPMC12874700

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.