Modelling responses of cotton growth and yield to pre-planting soil moisture with the CROPGRO-Cotton model for a mulched drip irrigation system in the Tarim Basin

Wang, Xingpeng; Wang, Hongbo; Si, Zhuanyun; Gao, Yang; Duan, Aiwang

doi:10.1016/j.agwat.2020.106378

Cited by 24 publications

(17 citation statements)

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“…In the research, the simulated errors of the calibrated WOFOST for emergence, flowering, and maturity were +1 days, +2 days, and +1 days, respectively, showing good simulation accuracy (2.74% error). Existing studies have shown that the average error of phenological development time simulated by calibrated CROPGRO-Cotton in southern Xinjiang is 2.52% [48], which is close to the simulation accuracy of this study. Other research results show that calibrated CROPGRO-Cotton fairly predicts phenological events such as anthesis and physiological maturity with RMSE of ≤2.27 and ≤4.98 d, respectively, in northwestern India [49], and absolute relative errors of 1.7%, 1.1%, and 1.1% based on modified CROPGRO-Cotton.…”

Section: Simulation Performance Analysis Of the Calibrated Wofost Mod...supporting

confidence: 88%

Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang

Wang

et al. 2022

Agriculture

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Planting with non-film mulching is the fundamental means to eliminate the pollution of residual film in cotton fields. However, this planting approach should have regional adaptability. Therefore, the calibrated WOFOST model and an early mature cultivar CRI619 (Gossypium hirsutum Linn) were employed to simulate the cotton growth, and regions were then evaluated for planting in Xinjiang. A field experiment was conducted in 2019–2020 at the experimental irrigation station of Alar City, and the data were used to calibrate and validate the WOFOST model. The field validation results showed that the errors of the WOFOST simulation for emergence, flowering, and maturity were +1 day, +2 days, and +1 day, respectively, with good simulation accuracy of phenological development time. The simulated WLV, WST, WSO, and TAGP agreed well with measured values, with R2 = 0.96, 0.97, 0.99, and 0.99, respectively. The RMSE values of simulated versus measured WLV, WST, WSO, and TAGP were 175, 210, 199, and 251 kg ha−1, and showed high accuracy. The simulated soil moisture (SM) agreed with the measured value, with R2 = 0.87. The calibration model also showed high SM simulation accuracy, with RMSE = 0.022 (cm3 cm−3). Under all treatments, the simulated TAGP and yield agreed well with the measured results, with R2 of 0.76 and 0.70, respectively. RMSE of simulated TAGP and yield was 465 and 200 kg ha−1, and showed high accuracy. The percentage RMSE values (ratio of RMSE to the average measured value, NRMSE) of ETa and WUE were 9.8% and 11.7%, indicating extremely high precision (NRMSE < 10%) and high precision (10% < NRMSE ≤ 20%), respectively. The simulated results for phenology length at the regional scales showed that the effective accumulation temperature in counties such as Yingjisha and Luntai was not enough for the phenological maturity of the studied cotton cultivar. The southern area of Xinjiang had a generally higher yield than the northern area but required more irrigation. This research can provide a method for evaluating the adaptability of filmless cultivation techniques for cotton in different counties.

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Section: Simulation Performance Analysis Of the Calibrated Wofost Mod...supporting

confidence: 88%

Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang

Wang

et al. 2022

Agriculture

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“…Os procedimentos gerais de calibração e definição dos parâmetros de entrada do modelo são semelhantes entre estudos com o CROPGRO, que começa com os coeficientes iniciais que são extraídos do arquivo de genótipo do DSSAT e seleciona o melhor valor para cada coeficiente avaliando o erro quadrático médio (RMSE) entre as variáveis simuladas e observadas (Alderman et al, 2015;BAO et al, 2015;Bosi et al, 2020;Wang et al, 2020)…”

Section: Modelo Crop Simulation Model -Cropgrounclassified

“…Geralmente, a produtividade de biomassa e a cobertura do dossel são os principais fatores de comparações. Os valores simulados e observados são submetidos a análises estatísticas, afim de evidenciar a precisão e confiança do modelo (Bosi et al, 2020;Wang et al, 2020).…”

Section: Xrftunclassified

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Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão

Pinheiro¹,

Souza²,

Jardim³

et al. 2021

Rev. Bras. Geog. Fis.

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O efeito climático é o principal responsável pelas oscilações no rendimento produtivo. Logo, é esperado que as mudanças do clima promovam alterações na agricultura, comprometam a sustentabilidade e a segurança alimentar, especialmente, em áreas semiáridas. O entendimento da amplitude desses fatores e suas consequências no rendimento agrícola mediante os diferentes cenários climáticos, regionais e tecnológicos são fundamentais nas tomadas de decisões. Para as análises desses diversos cenários, os modelos de simulação de culturas se caracterizam como ferramentas funcionais e com eficientes performances na estimativa dos níveis de produtividades, desde que devidamente calibrados e validados com dados consistentes e representativos. Dentre os modelos de simulação podemos destacar: AquaCrop - FAO, ZAE - FAO, CROPGRO e Apsim como aqueles de maiores aplicabilidades nas culturas agrícolas, sendo utilizados de maneira recorrente em diversos estudos para fins do conhecimento das lacunas de produtividade agrícola, ou “Yield Gap”. Esta revisão analisou os impactos das alterações climáticas na agricultura e o levantamento de informações dos principais modelos de simulação de culturas. Mediante síntese das informações levantadas, pode-se evidenciar o eminente impacto das alterações climáticas sobre o cenário agrícola futuro, proporcionando maior vulnerabilidade agrícola. Logo, destaca-se a importância do uso de modelos de simulação de culturas para conhecimento das lacunas de produtividade e potencial produtivo. Contudo, é evidente a necessidade de pesquisas mais detalhadas sobre a aplicabilidade dos modelos em cenários agrícolas diversos e situações climáticas amplas.Palavras-chave: modelos de simulação; sazonalidade climática; práticas resilientes; “yield gap”. Importance of crop simulation models in view of the impacts of climate change on agricultural production – Review A B S T R A C TThe climatic effect is the main responsible for the fluctuations in the productive yield. Therefore, it is expected that climate change will promote changes in agriculture, compromise sustainability and food security, especially in semi-arid areas. Understanding the breadth of these factors and their consequences on agricultural income through different climatic, regional and technological scenarios are fundamental in decision-making. For the analysis of these different scenarios, the crop simulation models are characterized as functional tools and with efficient performances in the estimation of the productivity levels, as long as they are properly calibrated and validated with consistent and representative data. Among the simulation models we can highlight: AquaCrop - FAO, ZAE - FAO, CROPGRO and Apsim as those with the greatest applicability in agricultural crops, being used in a recurring manner in several studies for the purpose of understanding agricultural productivity gaps, or “Yield Gap”. This review analyzed the impacts of climate change on agriculture and the gathering of information on the main crop simulation models. By synthesizing the information collected, it is possible to highlight the imminent impact of climate change on the future agricultural scenario, providing greater agricultural vulnerability. Therefore, the importance of using crop simulation models to understand the gaps in productivity and productive potential is highlighted. However, there is a clear need for more detailed research on the applicability of models in diverse agricultural scenarios and broad climatic situations.Keywords: simulation models; climatic seasonality; resilient practices; yield gap.

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“…M. Li et al (2018) and X. Wang et al (2020) used the experimentally determined soil evaporation reduction coefficient under film mulching conditions to reflect the effect of mulching on soil evaporation in the DSSAT model simulation. In fact, film mulching will reduce the potential evaporation capacity of the soil and further reduce the actual evaporation of the soil; meanwhile, the determination of the film mulching evaporation coefficient is empirical.…”

Section: Introductionmentioning

confidence: 99%

A modified DSSAT‐CERES model for simulating summer maize growth under film mulching

et al. 2021

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Plastic film mulching is an effective agronomic measure for increasing crop yield, and the decision support system for agricultural technology transfer (DSSAT) model is an effective tool for simulating crop growth and water management. However, the DSSAT model does not have a specific soil evaporation module for plastic film mulching. In this paper, a method for estimating soil evaporation capacity under film mulching conditions based on the small hole effect was developed and embedded in the DSSAT model. Then, considering the compensation effect of soil temperature on air temperature under the condition of film mulching, the DSSAT model was modified accordingly. The results show that the average temperature compensation coefficient for 2 yr from sowing to emergence was 0.695 ˚C, and 0.305 ˚C from emergence to jointing. The improved DSSAT model can simulate the growth of summer maize (Zea mays L.) and changes in soil water content. Compared with the original model, the average relative RMSE of the simulated and observed soil water content of the modified DSSAT model improved by 2.26 and 1.63%, respectively. The modified model can improve the simulation accuracy, which shows that the method proposed in this paper can provide an effective way for the simulation of crop growth process under film mulching conditions.

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Modelling responses of cotton growth and yield to pre-planting soil moisture with the CROPGRO-Cotton model for a mulched drip irrigation system in the Tarim Basin

Cited by 24 publications

References 10 publications

Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang

Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang

Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão

A modified DSSAT‐CERES model for simulating summer maize growth under film mulching

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