Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?

Quiroz, Roberto; Loayza, Hildo; Barreda, Carolina; Gavilán, C.; Posadas, Adolfo; Ramírez, David A.

doi:10.1016/j.eja.2016.10.008

Cited by 26 publications

(29 citation statements)

References 47 publications

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“…On the basis of the principle and structure of the original model, the corresponding parameters were modi ed to conform to the growth characteristics of potato, and the growth process of potato was simulated, so as to output the physiological characteristic parameters and yield data and realize the model simulation function. Quiroz et al [3] proposed that the incorporation of remotely sensed data in crop growth models with different temporal resolution and level of complexity could help to improve the yield estimation in potato. Moreover, it was identi ed that LAI at the initiation of stem elongation stage was closely related to yield, thus the remote estimation of LAI at this stage could be used to indicate the yield in oilseed rape [36].…”

Section: Discussionmentioning

confidence: 99%

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Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Luo

et al. 2020

Preprint

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Background The accurate estimation of potato yield at regional scale is crucial for food security, precision agriculture and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative LAI (rLAI) data to improve the accuracy of potato yield estimation based on weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with single stage and weighted stage was completed. Results The results showed that among the six test rVIs (rNDVI, rCIred edge, rCIgreen, rEVI2, rNDRE and rMTCI), rCIred edge was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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Section: Discussionmentioning

confidence: 99%

“…Potato (Solanum tuberosum L.), a mixed grain, forage and vegetable crop [1], is the fourth most important crop in the world [2][3]. Since the launch of the potato staple food strategy in 2015 in China, potato has become another major staple food crop after rice, wheat and corn [4].…”

Section: Introductionmentioning

confidence: 99%

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Luo

et al. 2020

Preprint

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“…Potato disease modelling, foresight, further model development Kroschel et al, 2013 [167]; Sporleder et al, 2013 [168]; Condori et al, 2014 [169]; Carli et al, 2014 [170]; Kleinwechter et al, 2016 [171]; Kroschel et al, 2017 [172]; Fleisher et al, 2017 [173]; Raymundo et al, 2017 [174]; Raymundo et al, 2017 [175]; Quiroz et al, 2017 [176]; Ramirez et al, 2017 [177]; Mujica et al, 2017 [178]; Scott and Kleinwechter, 2017 [179]; Petsakos et al, 2018 [180] AfricaRice: Model improvement, yield gap analysis, genotype × environment interactions, impact of climate change van Oort et al, 2014 [181]; van Oort et al, 2015 [182]; van Oort et al, 2015 [183]; Dingkuhn et al, 2015 [184]; van Oort et al, 2016 [185]; El-Namaky and van Oort, 2017 [186]; van Oort et al, 2017 [187]; Dingkuhn et al, 2017 [104,105]; van Oort and Zwart, 2018 [188]; van Oort, 2018 [189], Duku et al, 2018 [190] ICRAF: Agroforestry and intercropping modelling Africa Luedeling et al, 2014 [191]; Araya et al, 2015 [192]; Luedeling et al, 2016 [193]; Smethurst et al, 2017 [194], Masikati et al, 2017 [195] ILRI: crop-livestock-farm interactions Van Wijk et al, 2014 [196]; Herrero et al, 2014 [197] IITA: Modelling on Yams in West Africa Marcos et al, 2011 [198]; Cornet et al, 2015 [199]; Cornet et al, 2016 [200] ICARDA: Climate variability and change impact studies, foresight, conservation agriculture impact, genotype × environment interactions Sommer et al, 2013 [201]; Bobojonov and Aw-Hassan, 2014…”

Section: Cipmentioning

confidence: 99%

Role of Modelling in International Crop Research: Overview and Some Case Studies

et al. 2018

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Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers.

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“…速率, 使RuBP浓度不足而限制光合速率, 即RuBP 再生速率限制阶段 (Farquhar et al, 1980;von Caemmerer & Farquhar, 1981;Harley & Sharkey, 1991;von Caemmerer, 2000) (Miao et al, 2009)可以由以下公式计算得到, 即: Miao et al, 2009;Yin et al, 2009; 唐星林等, 2017a)。模型II较全面地考虑了光合作用的原初反应过程 (Ye et al, 2013a(Ye et al, , 2013b, 可定量研究与植物光合作用原初反应过程中的激子传递效率和捕光色素分子的参数变化情况以及这些参数对光的响应问题, 并且用其中的参数可以讨论植物的PSII动力学下调 (Ralph & Gademann, 2005;Kirchhoff et al, 2007;Brading et al, 2011) (Ježilová et al, 2015;Mayoral et al, 2015;Park et al, 2016;Bellucco et al, 2017;Quiroz et al,…”

Section: 表3 分配到碳同化和光呼吸途径的光合电子流unclassified

“…Chinese Journal of Plant Ecology, 42, 498-507. DOI: 10.17521/cjpe.2017.0320 植物的光合作用受光强、CO 2 浓度和温度等环境因子的影响。Farquhar等(1980)以及其他学者 (von Caemmerer & Farquhar, 1981;Harley & Sharkey, 1991;von Caemmerer, 2000von Caemmerer, , 2013根据核酮糖-1,5-二磷酸羧化酶/加氧酶(Rubisco)酶动力学反应和核酮糖-1,5-二磷酸(RuBP)再生反应化学计量学, 提出 C 3 植物光合生化模型(简称FvCB模型)。现在, FvCB 模型因能描述稳态的碳同化过程且具有明确的生物学意义而被广泛应用于光合作用研究 (Dubois et al, 2007;Farquhar & Busch, 2017)。生化模型由描述Rubisco酶活性限制、RuBP再生限制和磷酸丙糖利用率(TPU)限制等3个过程的子模型构成。其中非直角双曲线模型(简称模型I)是生化模型的主要子模型 (Long & Bernacchi, 2003;Dubois et al, 2007;Farquhar & Busch, 2017; 梁星云和刘世荣, 2017; 唐星林等, 2017a, 2017b)。在植物光合作用对光响应曲线(A n -I曲线, I为光合有效辐射) 的拟合中, 模型I得到广泛的应用和验证, 但由此模型拟合光响应曲线得到的最大净光合速率(A nmax )显著高于实测值 (Calama et al, 2013;王荣荣等, 2013;冷寒冰等, 2014;Ježilová et al, 2015;Mayoral et al, 2015;Park et al, 2016;Bellucco et al, 2017;Quiroz et al, 2017), 这将高估植物的光合能力; 并且该模型不能拟合植物发生光抑制时的光响应曲线 (Ye, 2007;dos Santos et al, 2013;王海珍等, 2017) (Cheng et al, 2001;Long & Bernacchi, 2003 (Serôdio et al, 2013;Li et al, 2015Li et al, , 2018Sun et al, 2015;Gao et al, 2017aGao et al, , 2017bShimada et al, 2017)…”

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Determination of maximum electron transport rate and its impact on allocation of electron flow

Ye¹,

Shi-Hua²,

An³

et al. 2018

Chinese Journal of Plant Ecology

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Aims The non-rectangular hyperbolic model (termed as model I) is the main submodel of the FvCB biochemical model, which is used to estimate the maximum electron transport rate (J max) of plant leaves. The submodel is widely applied to fit the light-response curves of electron transport rate (J-I curves), and obtain J max. However, it has not been strictly verified whether J max calculated by model I is consistent with the measured values. Methods Light-response curves of electron transport rate and of photosynthesis rate of soybean (Glycine max) (under shading and full sunlight) were simultaneously measured by LI-6400-40, then these data were simulated by model I and the mechanistic model of light-response of electron transport rate (termed as model II). Important findings The results showed that there was the significant differences between J max estimated by model I and the observation data irrespective of shading and full sunny leaves of soybean. However, there was no significant difference between J max calculated by model II and the measured value. Because J max was overestimated by model I, it must lead to overestimate the amount of photosynthetic electron flow to allocate to photorespiration pathway, and magnify the photoprotection of photorespiration on plants. On the contrary, the J max and saturation light intensity (PAR sat) obtained by the model II were in very close agreement with the observations. It can be concluded that the model II was superior to the model I in estimates of J max and PAR sat. Therefore, we recommend model II to be used as an operational model for fitting J-I curves and accurately assess the role of photorespiration on plant photo-protection.

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Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?

Cited by 26 publications

References 47 publications

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Role of Modelling in International Crop Research: Overview and Some Case Studies

Determination of maximum electron transport rate and its impact on allocation of electron flow

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