Melon is a crucial cultivar in the Cucurbitaceae family and is the third most-produced in the world (FAO, 1994). Thus, different phytochemical regulators have been examined to produce high-quality fruits, develop labor-saving and low-cost cultivation methods: promotion of fruit set by synthetic cytokinins, 6-benzylaminopurine (BA) (Jones, 1965), and acceleration of fruit set by 1(2-Chloro4-pyridinyl) -3-phenylurea (CPPU) (Li et al., 2002); increase of sucrose concentration by synthetic auxin, pchlorophenoxyacetic acid (p-CPA) (Hayata et al., 2002). Additionally, the mechanism of this formation is not clear despite a unique net is generated in Earls melons. Fruit growth is regulated by various plant hormones (Gillaspy et al. 1993). Therefore, analysis of the endogenous amounts of major plant hormones will lead to the elucidation of developmental physiology and the development of techniques for overcoming physiological disorders. The following studies are known on the levels of endogenous hormones in melon fruits: analysis of indole-3acetic acid (IAA) at 5─35DAF (days after flowering) in seeds and pulp by high performance liquid chromatography (HPLC) with a fluorescence detector (Lee et al., 1997); analysis of abscisic acid (ABA) at 20─60 DAF seeds and pulp by enzyme linked immune sorbent assay (ELISA) (Welbaum et al., 2000); gas chromatographymass spectrometry (GC-MS) analysis of IAA and ABA for 10 days after treatment with synthetic cytokinin on flowering (Hayata et al., 2002). It has also reported that analysis of IAA, ABA and gibberellin (GA) in the rind, pulp and placenta of seeded and non-seeded grape cultivars by HPLC (Wang et al., 1993). However, there is no report on the simultaneous analysis of major phytohormones of each tissue in growing melon fruit by the current reliable mass detector. Therefore, in this study, eight major endogenous hormones, IAA, ABA, trans-zeatin (tZ), isopentenyl adenine (iP), jasmonic acid (JA), methyl jasmonate (MeJA), and GA (GA1, GA4) were concurrently quantified by instrumental analysis. This study is elucidate the phytohormone profiles at various stages during melon fruit growth.
甜瓜是葫芦科的重要品种，是世界上产量第三大的品种(粮农组织，1994年)。因此，研究人员研究了不同的植物化学调节剂，以生产高质量的水果，开发节省劳动力和低成本的栽培方法:通过合成细胞分裂素，6-苄基嘌呤(BA)促进坐果(Jones, 1965)，以及1(2-氯- 4-吡啶基)-3-苯脲(CPPU)加速坐果(Li et al.， 2002);通过合成生长素，氯苯氧乙酸(p-CPA)增加蔗糖浓度(Hayata等，2002)。此外，尽管在伯爵甜瓜中产生了独特的网，但这种形成的机制尚不清楚。果实生长受多种植物激素的调控(Gillaspy et al. 1993)。因此，分析主要植物激素的内源量将有助于阐明发育生理学和开发克服生理障碍的技术。关于甜瓜果实内源激素水平的已知研究有:用荧光检测器高效液相色谱法(HPLC)分析种子和果肉中5─35DAF(开花后几天)时的吲哚-3乙酸(IAA) (Lee et al.， 1997);用酶联免疫吸附试验(ELISA)分析20─60个DAF种子和果肉中的脱落酸(ABA) (Welbaum等人，2000);合成细胞分裂素处理开花后10天的IAA和ABA的气相色谱-质谱(GC-MS)分析(Hayata et al.， 2002)。也有报道用高效液相色谱法分析有籽和无籽葡萄品种的果皮、果肉和胎座中的IAA、ABA和赤霉素(GA) (Wang et al.， 1993)。然而，目前尚无可靠的质量检测器同时分析甜瓜果实生长过程中各组织主要激素的报道。因此，本研究采用仪器分析方法，同时定量测定了8种主要内源激素IAA、ABA、反式玉米素(tZ)、异戊烯基腺嘌呤(iP)、茉莉酸(JA)、茉莉酸甲酯(MeJA)和GA (GA1、GA4)。本研究旨在阐明甜瓜果实生长不同阶段的植物激素分布。
{"title":"Endogenous Plant Hormone Profiles in Growing Melon Fruit","authors":"K. Kojima, Hayata Nomura, Daigo Andou","doi":"10.2525/ecb.59.141","DOIUrl":"https://doi.org/10.2525/ecb.59.141","url":null,"abstract":"Melon is a crucial cultivar in the Cucurbitaceae family and is the third most-produced in the world (FAO, 1994). Thus, different phytochemical regulators have been examined to produce high-quality fruits, develop labor-saving and low-cost cultivation methods: promotion of fruit set by synthetic cytokinins, 6-benzylaminopurine (BA) (Jones, 1965), and acceleration of fruit set by 1(2-Chloro4-pyridinyl) -3-phenylurea (CPPU) (Li et al., 2002); increase of sucrose concentration by synthetic auxin, pchlorophenoxyacetic acid (p-CPA) (Hayata et al., 2002). Additionally, the mechanism of this formation is not clear despite a unique net is generated in Earls melons. Fruit growth is regulated by various plant hormones (Gillaspy et al. 1993). Therefore, analysis of the endogenous amounts of major plant hormones will lead to the elucidation of developmental physiology and the development of techniques for overcoming physiological disorders. The following studies are known on the levels of endogenous hormones in melon fruits: analysis of indole-3acetic acid (IAA) at 5─35DAF (days after flowering) in seeds and pulp by high performance liquid chromatography (HPLC) with a fluorescence detector (Lee et al., 1997); analysis of abscisic acid (ABA) at 20─60 DAF seeds and pulp by enzyme linked immune sorbent assay (ELISA) (Welbaum et al., 2000); gas chromatographymass spectrometry (GC-MS) analysis of IAA and ABA for 10 days after treatment with synthetic cytokinin on flowering (Hayata et al., 2002). It has also reported that analysis of IAA, ABA and gibberellin (GA) in the rind, pulp and placenta of seeded and non-seeded grape cultivars by HPLC (Wang et al., 1993). However, there is no report on the simultaneous analysis of major phytohormones of each tissue in growing melon fruit by the current reliable mass detector. Therefore, in this study, eight major endogenous hormones, IAA, ABA, trans-zeatin (tZ), isopentenyl adenine (iP), jasmonic acid (JA), methyl jasmonate (MeJA), and GA (GA1, GA4) were concurrently quantified by instrumental analysis. This study is elucidate the phytohormone profiles at various stages during melon fruit growth.","PeriodicalId":85505,"journal":{"name":"Seibutsu kankyo chosetsu. [Environment control in biology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41496104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Damage caused by intumescence has recently become a serious issue in tomato-producing areas (Wu et al., 2017; Misu et al., 2018). Intumescence is a physiological disorder and non-pathogenic disease that occurs in many plant species (Eguchi et al., 2016). These species include members of the Solanaceae family, such as the eggplant (Solanum melongena) (Eisa and Dobrenz, 1971), potato (Solanum tuberosum L.) (Petitte and Ormrod, 1986), and tomato (Solanum lycopersicum L.) (Lang et al., 1983). In tomato, intumescence causes abnormal outgrowths of the leaf epidermal and palisade parenchyma cell walls, and of the petiole or stem surfaces during early seedling growth or during cultivation after transplanting under greenhouse conditions. Blisters occur on the leaf abaxial surfaces in mild cases of intumescence (Fig. 1A, B), deformities of compound leaves can develop as the condition worsens (Fig. 1C, D), browning and necrosis appear in more severe cases, and leaf abscission occurs in extreme cases, resulting in a significant decrease in growth. It has been reported that there are differences between varieties in the occurrence of tomato intumescence (Ozawa et al., 2018). However, it is not clear why there are differences between varieties. Although intumescence reportedly results from cell hypertrophy and rupture (Balge et al., 1969; Eisa and Dobrenz, 1971; Lang and Tibbitts, 1983; Lang et al., 1983; Wetzstein and Frett, 1984; Pinkard et al., 2006; Craver et al., 2014; Suzuki et al., 2020), the underlying causes are not yet fully understood. Previous studies have indicated that a high relative humidity, high root medium water content, or a combination thereof are the causes of intumescence (Metwally et al., 1970; Eisa and Dobrenz, 1971; Misu et al., 2018). These reports have suggested that excess turgor pressure may be the primary cause of intumescence. Since intumescence involves the swelling and rupture of cell walls, it is likely that sudden variations in the plant water potential will influence the onset of intumescence. Plant water potential has been shown to be closely related to the water environment (Kramer and Boyer, 1995). For example, the water potential of tomato plants has been shown to be affected by the relative humidity and soil moisture content when grown in controlled climate chambers (Araki, 1993), and by attributes of the water environment, including weather and soil water suction pressure (pF value), during cultivation under field conditions (Fusao, 2003). Furthermore, the water potential of tomato plants is affected by water absorption and transpiration rates, as influenced by atmospheric and soil water potentials (Zhang et al., 2017). Lang and Tibbitts (1983), however, reported no differences in intumescence incidence at relative humidity levels of 30%, 80%, and 92%. Considering these findings, we proposed that intumescence does not occur merely owing to the persistence of high levels of humidity and soil moisture content, but rathe
膨胀引起的损害最近已成为番茄产区的一个严重问题（Wu等人，2017；Misu等人，2018）。膨胀症是一种发生在许多植物物种中的生理障碍和非致病性疾病（Eguchi等人，2016）。这些物种包括茄科的成员，如茄子（Solanum melongena）（Eisa和Dobrenz，1971）、土豆（Solanum-tuberosum L.）（Petite和Ormrod，1986）和番茄（Solanul-lycopersicum L.）（Lang等人，1983）。在番茄中，在幼苗早期生长或在温室条件下移植后的培养过程中，膨胀会导致叶表皮和栅栏薄壁细胞壁以及叶柄或茎表面的异常生长。在轻度膨胀的情况下，叶片背面会出现水泡（图1A，B），随着病情的恶化，复叶会出现畸形（图1C，D），在更严重的情况下会出现褐变和坏死，在极端情况下会发生叶片脱落，导致生长显著下降。据报道，番茄膨胀症的发生在不同品种之间存在差异（Ozawa等人，2018）。然而，目前尚不清楚为什么品种之间存在差异。尽管据报道膨胀是由细胞肥大和破裂引起的（Balge等人，1969；Eisa和Dobrenz，1971；Lang和Tibbitts，1983；Lang等人，1983；Wetzstein和Frett，1984；Pinkard等人，2006；Craver等人，2014；Suzuki等人，2020），但其根本原因尚不完全清楚。先前的研究表明，高相对湿度、高根系介质含水量或其组合是膨胀的原因（Metwally等人，1970；Eisa和Dobrenz，1971；Misu等人，2018）。这些报告表明，过度的膨压可能是膨胀的主要原因。由于膨胀涉及细胞壁的膨胀和破裂，植物水势的突然变化很可能会影响膨胀的开始。植物水势已被证明与水环境密切相关（Kramer和Boyer，1995）。例如，番茄植物的水势已被证明受到在受控气候室中生长时的相对湿度和土壤含水量的影响（Araki，1993），以及在田间条件下种植期间的水环境属性的影响，包括天气和土壤吸水压力（pF值）（扶桑，2003）。此外，番茄植物的水势受到水分吸收和蒸腾速率的影响，也受到大气和土壤水势的影响（Zhang et al.，2017）。然而，Lang和Tibbitts（1983）报告称，在30%、80%和92%的相对湿度水平下，膨胀发生率没有差异。考虑到这些发现，我们认为膨胀的发生不仅仅是因为高湿度和土壤含水量的持续存在，而是因为这些环境变量从低到高的突然波动。番茄品种对水环境的变化表现出不同的水势反应（托雷西拉
{"title":"Varietal Differences in Tomato Intumescence under Changing Water Conditions","authors":"Y. Miyama, Nanako Yasui","doi":"10.2525/ecb.59.157","DOIUrl":"https://doi.org/10.2525/ecb.59.157","url":null,"abstract":"Damage caused by intumescence has recently become a serious issue in tomato-producing areas (Wu et al., 2017; Misu et al., 2018). Intumescence is a physiological disorder and non-pathogenic disease that occurs in many plant species (Eguchi et al., 2016). These species include members of the Solanaceae family, such as the eggplant (Solanum melongena) (Eisa and Dobrenz, 1971), potato (Solanum tuberosum L.) (Petitte and Ormrod, 1986), and tomato (Solanum lycopersicum L.) (Lang et al., 1983). In tomato, intumescence causes abnormal outgrowths of the leaf epidermal and palisade parenchyma cell walls, and of the petiole or stem surfaces during early seedling growth or during cultivation after transplanting under greenhouse conditions. Blisters occur on the leaf abaxial surfaces in mild cases of intumescence (Fig. 1A, B), deformities of compound leaves can develop as the condition worsens (Fig. 1C, D), browning and necrosis appear in more severe cases, and leaf abscission occurs in extreme cases, resulting in a significant decrease in growth. It has been reported that there are differences between varieties in the occurrence of tomato intumescence (Ozawa et al., 2018). However, it is not clear why there are differences between varieties. Although intumescence reportedly results from cell hypertrophy and rupture (Balge et al., 1969; Eisa and Dobrenz, 1971; Lang and Tibbitts, 1983; Lang et al., 1983; Wetzstein and Frett, 1984; Pinkard et al., 2006; Craver et al., 2014; Suzuki et al., 2020), the underlying causes are not yet fully understood. Previous studies have indicated that a high relative humidity, high root medium water content, or a combination thereof are the causes of intumescence (Metwally et al., 1970; Eisa and Dobrenz, 1971; Misu et al., 2018). These reports have suggested that excess turgor pressure may be the primary cause of intumescence. Since intumescence involves the swelling and rupture of cell walls, it is likely that sudden variations in the plant water potential will influence the onset of intumescence. Plant water potential has been shown to be closely related to the water environment (Kramer and Boyer, 1995). For example, the water potential of tomato plants has been shown to be affected by the relative humidity and soil moisture content when grown in controlled climate chambers (Araki, 1993), and by attributes of the water environment, including weather and soil water suction pressure (pF value), during cultivation under field conditions (Fusao, 2003). Furthermore, the water potential of tomato plants is affected by water absorption and transpiration rates, as influenced by atmospheric and soil water potentials (Zhang et al., 2017). Lang and Tibbitts (1983), however, reported no differences in intumescence incidence at relative humidity levels of 30%, 80%, and 92%. Considering these findings, we proposed that intumescence does not occur merely owing to the persistence of high levels of humidity and soil moisture content, but rathe","PeriodicalId":85505,"journal":{"name":"Seibutsu kankyo chosetsu. [Environment control in biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47405656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Utarbayeva, S. Aipeisova, Adilkhan Maui, E. Kazkeev, G. Bimagambetova, Z. Kukenov
Various characteristics of plant organs and pollen contribute to biological diversity (Hwang and Masters, 2013) and data on their variability lay groundwork for the solution of many biological problems related to taxonomy, microevolutionary processes, hybridization, and gene pool protection (Polyakova and Gataulina, 2008; Warny, 2013). Pollen analysis or the study of pollen grain morphology (i.e., size, exine pattern, fertility, and viability) is a research method for measuring the reproductive potential of plants (Goodman et al., 2015). Pollen analysis deals with the estimation of normal and defective pollen fractions and with metabolism processes in pollen grains (Pausheva, 1980; Riley et al., 2015). The problem of multiple anomalies in pollen grains has been a forefront in recent debates (Augenstein, 2016; Miroff, 2019). The quality of pollen grains is crucial to the reproductive biology of plants and their ability to generate full-fledged seeds (Laurence, 2018; Laurence and Bryant, 2019). Normally, pollen in plants growing under normal conditions is of good quality and the amount of normal pollen grains is close to 100% (Bryant and Bryant, 2019). Increased pollution, on the contrary, can reduce their proportion (Ashikhmina, 2005). Recent decades have saw a steady growth in various sectors of economy, which strengthened the role of anthropogenic factors, both biotic and abiotic (Legendre and Legendre, 2012). Cities with industrial zones functioning side by side with the green zones can serve a convenient model object in the judgment of pollution levels. Such cities can be found in many countries around the world, including China, Kazakhstan, Russia, and the United States. At the same time, the Western European countries have a strong experience in designing urban landscapes with ecological considerations in mind, something that developing countries, such as Kazakhstan, may need to consider. The widely used landscape plants are metal tolerant, mostly woody, such as the black poplar (Populus nigra L.). The most solid and gas emissions concentrate in the air 15 to 20 m above the ground. This range of polluted air travel is the living zone for humans and most plant species (Ferguson et al., 2018). Therefore, research on the effective monitoring of pollutants in green zones is needed (Faucon et al., 2017). This study chose plant pollen as a research object for its sensitivity to pollution and anthropogenic loads of different intensity. This kind of data is somewhat scarce, which defined the relevance of this study. It is known that pollen formation takes place through many cell divisions, which vary among different plant species (He et al., 2019). This statement equally applies to pol-
植物器官和花粉的各种特征有助于生物多样性(Hwang and Masters, 2013)，其变异性数据为解决与分类学、微进化过程、杂交和基因库保护相关的许多生物学问题奠定了基础(Polyakova and Gataulina, 2008;Warny, 2013)。花粉分析或研究花粉粒形态(即大小、外叶格局、育性和活力)是测量植物生殖潜力的一种研究方法(Goodman et al.， 2015)。花粉分析涉及正常和缺陷花粉组分的估计以及花粉粒中的代谢过程(Pausheva, 1980;Riley et al.， 2015)。花粉粒的多重异常问题一直是最近争论的前沿(Augenstein, 2016;Miroff, 2019)。花粉粒的质量对植物的生殖生物学及其产生成熟种子的能力至关重要(Laurence, 2018;劳伦斯和布莱恩特，2019)。正常情况下生长的植物花粉质量较好，正常花粉粒的数量接近100% (Bryant and Bryant, 2019)。相反，污染的增加可以减少他们的比例(Ashikhmina, 2005)。近几十年来，各个经济部门稳步增长，这加强了人为因素的作用，包括生物和非生物因素(Legendre和Legendre, 2012)。工业区与绿化区并驾齐驱的城市可以作为判断污染程度的便捷模型对象。这样的城市在世界上许多国家都可以找到，包括中国、哈萨克斯坦、俄罗斯和美国。与此同时，西欧国家在考虑生态因素的城市景观设计方面有着丰富的经验，这是哈萨克斯坦等发展中国家可能需要考虑的。广泛使用的景观植物是耐金属的，主要是木本植物，如黑杨树(Populus nigra L.)。大多数固体和气体排放集中在离地面15至20米的空气中。这一受污染的空气旅行范围是人类和大多数植物物种的生活区(Ferguson et al.， 2018)。因此，需要对绿区污染物的有效监测进行研究(Faucon et al.， 2017)。本研究选择植物花粉作为研究对象，是因为花粉对不同强度的污染和人为负荷的敏感性。这类数据有些稀缺，这就定义了本研究的相关性。众所周知，花粉的形成是通过许多细胞分裂进行的，这在不同的植物物种中是不同的(He et al.， 2019)。这个说法同样适用于pol-
{"title":"Pollen Morphology of Broadleaf Trees Growing in Different Health Conditions in the City of Aktobe","authors":"N. Utarbayeva, S. Aipeisova, Adilkhan Maui, E. Kazkeev, G. Bimagambetova, Z. Kukenov","doi":"10.2525/ecb.59.135","DOIUrl":"https://doi.org/10.2525/ecb.59.135","url":null,"abstract":"Various characteristics of plant organs and pollen contribute to biological diversity (Hwang and Masters, 2013) and data on their variability lay groundwork for the solution of many biological problems related to taxonomy, microevolutionary processes, hybridization, and gene pool protection (Polyakova and Gataulina, 2008; Warny, 2013). Pollen analysis or the study of pollen grain morphology (i.e., size, exine pattern, fertility, and viability) is a research method for measuring the reproductive potential of plants (Goodman et al., 2015). Pollen analysis deals with the estimation of normal and defective pollen fractions and with metabolism processes in pollen grains (Pausheva, 1980; Riley et al., 2015). The problem of multiple anomalies in pollen grains has been a forefront in recent debates (Augenstein, 2016; Miroff, 2019). The quality of pollen grains is crucial to the reproductive biology of plants and their ability to generate full-fledged seeds (Laurence, 2018; Laurence and Bryant, 2019). Normally, pollen in plants growing under normal conditions is of good quality and the amount of normal pollen grains is close to 100% (Bryant and Bryant, 2019). Increased pollution, on the contrary, can reduce their proportion (Ashikhmina, 2005). Recent decades have saw a steady growth in various sectors of economy, which strengthened the role of anthropogenic factors, both biotic and abiotic (Legendre and Legendre, 2012). Cities with industrial zones functioning side by side with the green zones can serve a convenient model object in the judgment of pollution levels. Such cities can be found in many countries around the world, including China, Kazakhstan, Russia, and the United States. At the same time, the Western European countries have a strong experience in designing urban landscapes with ecological considerations in mind, something that developing countries, such as Kazakhstan, may need to consider. The widely used landscape plants are metal tolerant, mostly woody, such as the black poplar (Populus nigra L.). The most solid and gas emissions concentrate in the air 15 to 20 m above the ground. This range of polluted air travel is the living zone for humans and most plant species (Ferguson et al., 2018). Therefore, research on the effective monitoring of pollutants in green zones is needed (Faucon et al., 2017). This study chose plant pollen as a research object for its sensitivity to pollution and anthropogenic loads of different intensity. This kind of data is somewhat scarce, which defined the relevance of this study. It is known that pollen formation takes place through many cell divisions, which vary among different plant species (He et al., 2019). This statement equally applies to pol-","PeriodicalId":85505,"journal":{"name":"Seibutsu kankyo chosetsu. [Environment control in biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44408054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yayu Romdhonah, N. Fujiuchi, N. Takahashi, H. Nishina, K. Takayama
There have been many works to improve productivity of greenhouse-grown tomato. One effort is to analyze the photosynthetic rate as it influences productivity (Hisaeda et al., 2007; Takayama et al., 2010). Thus, quantifying photosynthetic rates is essential to diagnose the plant condition as well as to achieve the optimum cultivation condition in the greenhouse in the speaking plant approach concept (Hashimoto, 1989; Udink ten Cate et al., 1978). To bridge the plant’s photosynthesis and greenhouse climate control, many researchers developed various kinds of mathematical models of the environmental response of photosynthesis. The models were used as research tools or analytical means, for forecasting, or to be implemented in a computerized system for climate control (Nederhoff and Vegter, 1994). Thornley’s model (Thornley, 1976), Acock’s model (Acock et al., 1978), TOMGRO (Dayan et al., 1993), and TOMSIM (Heuvelink, 1996) are some established models. Some studies used photosynthesis measurements of single-leaf (Thornley, 1976; Acock et al., 1978; Xin et al., 2019), other studies used canopy-level measurements in a closed chamber (Acock et al., 1978), or whole-greenhouse (Nederhoff and Vegter, 1994; Tsafaras and de Koning, 2017). However, these studies could not provide a realtime response of the photosynthesis of a full-size plant, as part of a community under greenhouse condition, to its environment. Furthermore, the variables used in the photosynthesis models vary among the models. The Thornley’s model used incident light flux density, ambient CO2 concentration, and dark respiration rate with three other parameters to calculate net photosynthesis of single leaf (Acock et al., 1978). The model was then constructed by Acock et al. (1978) for canopy photosynthesis in tomato. Nederhoff and Vegter (1994) used variables of photosynthetically active radiation (PAR, i.e., light flux, 400―700 nm), CO2 concentration, and leaf area index (LAI) in an empirical photosynthesis model. However, other environmental factors may also contribute to photosynthesis activity. Based on previous literature, photosynthesis activity has an apparent response to temperature (Castilla, 2013), and is affected by vapor pressure deficit (Acock et al., 1976; Shamshiri et al., 2018). The objective of the present study was to develop an empirical model for the estimation of the whole-plant net photosynthetic rate (Pn) of cherry tomato as a function of relevant greenhouse environmental factors. We used data of Pn at the whole-plant level as a real-time response to instantaneous PAR, air temperature, vapor pressure deficit,
已经有许多工作来提高温室种植番茄的生产力。一项努力是分析光合速率对生产力的影响（Hisaeda等人，2007年；Takayama等人，2010年）。因此，量化光合速率对于诊断植物状况以及在温室中实现最佳栽培条件至关重要（Hashimoto，1989；Udink-ten-Cate等人，1978年）。为了将植物的光合作用与温室气候控制联系起来，许多研究人员开发了各种光合作用环境响应的数学模型。这些模型被用作研究工具或分析手段，用于预测，或在气候控制的计算机系统中实施（Nederhoff和Vetter，1994）。Thornley模型（Thornley，1976）、Acock模型（Acock et al.，1978）、TOMGRO（Dayan et al.，1993）和TOMSIM（Heuvelink，1996）是一些已建立的模型。一些研究使用了单叶片的光合作用测量（Thornley，1976；Acock等人，1978年；Xin等人，2019），其他研究使用了密闭室（Acock et al.，1978）或整个温室中的冠层水平测量（Nederhoff和Vetter，1994；Tsafaras和de Koning，2017）。然而，作为温室条件下群落的一部分，这些研究无法提供全尺寸植物光合作用对环境的实时响应。此外，光合作用模型中使用的变量因模型而异。Thornley模型使用入射光通量密度、环境CO2浓度和暗呼吸率以及其他三个参数来计算单叶片的净光合作用（Acock等人，1978）。Acock等人（1978）构建了番茄冠层光合作用模型。Nederhoff和Vetter（1994）在经验光合作用模型中使用了光合作用活性辐射（标准杆数，即光通量，400–700 nm）、CO2浓度和叶面积指数（LAI）等变量。然而，其他环境因素也可能有助于光合作用的活性。根据先前的文献，光合作用活性对温度有明显的反应（Castilla，2013），并受到蒸汽压不足的影响（Acock等人，1976；Shamshiri等人，2018）。本研究的目的是建立一个经验模型，用于估计樱桃番茄全株净光合速率（Pn）与相关温室环境因素的关系。我们使用整体水平的Pn数据作为对瞬时标准杆数、空气温度、蒸汽压不足的实时响应，
{"title":"Empirical Model for the Estimation of Whole-plant Photosynthetic Rate of Cherry Tomato Grown in a Commercial Greenhouse","authors":"Yayu Romdhonah, N. Fujiuchi, N. Takahashi, H. Nishina, K. Takayama","doi":"10.2525/ecb.59.117","DOIUrl":"https://doi.org/10.2525/ecb.59.117","url":null,"abstract":"There have been many works to improve productivity of greenhouse-grown tomato. One effort is to analyze the photosynthetic rate as it influences productivity (Hisaeda et al., 2007; Takayama et al., 2010). Thus, quantifying photosynthetic rates is essential to diagnose the plant condition as well as to achieve the optimum cultivation condition in the greenhouse in the speaking plant approach concept (Hashimoto, 1989; Udink ten Cate et al., 1978). To bridge the plant’s photosynthesis and greenhouse climate control, many researchers developed various kinds of mathematical models of the environmental response of photosynthesis. The models were used as research tools or analytical means, for forecasting, or to be implemented in a computerized system for climate control (Nederhoff and Vegter, 1994). Thornley’s model (Thornley, 1976), Acock’s model (Acock et al., 1978), TOMGRO (Dayan et al., 1993), and TOMSIM (Heuvelink, 1996) are some established models. Some studies used photosynthesis measurements of single-leaf (Thornley, 1976; Acock et al., 1978; Xin et al., 2019), other studies used canopy-level measurements in a closed chamber (Acock et al., 1978), or whole-greenhouse (Nederhoff and Vegter, 1994; Tsafaras and de Koning, 2017). However, these studies could not provide a realtime response of the photosynthesis of a full-size plant, as part of a community under greenhouse condition, to its environment. Furthermore, the variables used in the photosynthesis models vary among the models. The Thornley’s model used incident light flux density, ambient CO2 concentration, and dark respiration rate with three other parameters to calculate net photosynthesis of single leaf (Acock et al., 1978). The model was then constructed by Acock et al. (1978) for canopy photosynthesis in tomato. Nederhoff and Vegter (1994) used variables of photosynthetically active radiation (PAR, i.e., light flux, 400―700 nm), CO2 concentration, and leaf area index (LAI) in an empirical photosynthesis model. However, other environmental factors may also contribute to photosynthesis activity. Based on previous literature, photosynthesis activity has an apparent response to temperature (Castilla, 2013), and is affected by vapor pressure deficit (Acock et al., 1976; Shamshiri et al., 2018). The objective of the present study was to develop an empirical model for the estimation of the whole-plant net photosynthetic rate (Pn) of cherry tomato as a function of relevant greenhouse environmental factors. We used data of Pn at the whole-plant level as a real-time response to instantaneous PAR, air temperature, vapor pressure deficit,","PeriodicalId":85505,"journal":{"name":"Seibutsu kankyo chosetsu. [Environment control in biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49129428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yayu Romdhonah, N. Fujiuchi, Kota Shimomoto, N. Takahashi, H. Nishina, K. Takayama
Sensor-based plant diagnostic technology is an essential feature of the speaking plant approach (Hashimoto, 1989; Takayama, 2013). A recent plant diagnosis technology, which has been started to be installed in practical agricultural production in greenhouses, is the real-time photosynthesis and transpiration monitoring system (Shimomoto et al., 2020). The system allows to remotely and continuously measure photosynthesis of whole plants under greenhouse conditions without any contact or intrusive. Also, data are sampled with high time-resolution, which are recorded in 5-minute intervals. With these features, the system is desirable for precise quantification of plant responses to stimuli at the whole-plant level. The numerous photosynthesis data produced by the photosynthesis monitoring system can be used for analysis and forecasting by way of modeling. Estimation models for the net photosynthetic rate (Pn) as a function of environmental factors for greenhouse tomato utilizing numerous data were limited to the whole greenhouse, such as the work of Nederhoff and Vegter (1994a). They used three variables of incident photosynthetically active radiation (PAR), CO2 concentration, with double rectangular hyperbolic relation and leaf area index (LAI) to calculate the net canopy photosynthesis rate of tomato greenhouse with an R of 0.892. In another work, they modified two established mechanical models of Acock (Acock et al., 1978) and Thornley (Thornley, 1976) to fit their data and gave Rs of 0.893 and 0.817, respectively (Nederhoff and Vegter, 1994b). On the other hand, the accuracy of sensor-based technology in modern greenhouses is jeopardized by disturbances, such as inaccuracy of the measurements by the sensor itself due to dynamic variations in the greenhouse climate (van Mourik et al., 2019), especially when measurements are performed in high time resolution. Therefore, it leads to an issue of how to address the high time-resolution data produced by the sensors for further use of model development. As most real-time data contain erroneous values and noise, such data required further processing for filtering noise and smoothing. Averaging techniques commonly used for smoothing data include the moving average (Čampulová, 2018) and the simple average (Yaffee and McGee, 2000). When the data are correctly prepared, the quality of the model can be reliable (Pyle, 1999). The objective of the present study was to process the
基于传感器的植物诊断技术是说话植物方法的一个基本特征(Hashimoto, 1989;高山,2013)。最近在温室实际农业生产中开始安装的植物诊断技术是光合和蒸腾实时监测系统(Shimomoto et al.， 2020)。该系统允许在没有任何接触或侵入的情况下，远程和连续测量温室条件下整个植物的光合作用。此外，数据以高时间分辨率采样，每5分钟记录一次。有了这些特点，该系统是在整个植物水平上精确量化植物对刺激的反应的理想选择。光合作用监测系统产生的大量光合作用数据可以通过建模的方式进行分析和预测。Nederhoff和Vegter (1994a)利用大量数据建立的温室番茄净光合速率(Pn)随环境因子变化的估算模型仅限于整个温室。利用入射光合有效辐射(PAR)、CO2浓度、双矩形双曲线关系和叶面积指数(LAI)三个变量，计算出番茄温室的净冠层光合速率，R为0.892。在另一项工作中，他们修改了Acock (Acock et al.， 1978)和Thornley (Thornley, 1976)的两个已建立的力学模型，以拟合他们的数据，Rs分别为0.893和0.817 (Nederhoff和Vegter, 1994b)。另一方面，现代温室中基于传感器的技术的准确性受到干扰的影响，例如由于温室气候的动态变化，传感器本身的测量不准确(van Mourik等人，2019)，特别是在高时间分辨率下进行测量时。因此，它导致了如何处理传感器产生的高时间分辨率数据以进一步使用模型开发的问题。由于大多数实时数据包含错误值和噪声，因此需要对这些数据进行进一步处理以滤除噪声和平滑。通常用于平滑数据的平均技术包括移动平均(Čampulová， 2018)和简单平均(Yaffee和McGee, 2000)。当数据准备正确时，模型的质量可以是可靠的(Pyle, 1999)。本研究的目的是处理
{"title":"Averaging Techniques in Processing the High Time-resolution Photosynthesis Data of Cherry Tomato Plants for Model Development","authors":"Yayu Romdhonah, N. Fujiuchi, Kota Shimomoto, N. Takahashi, H. Nishina, K. Takayama","doi":"10.2525/ecb.59.107","DOIUrl":"https://doi.org/10.2525/ecb.59.107","url":null,"abstract":"Sensor-based plant diagnostic technology is an essential feature of the speaking plant approach (Hashimoto, 1989; Takayama, 2013). A recent plant diagnosis technology, which has been started to be installed in practical agricultural production in greenhouses, is the real-time photosynthesis and transpiration monitoring system (Shimomoto et al., 2020). The system allows to remotely and continuously measure photosynthesis of whole plants under greenhouse conditions without any contact or intrusive. Also, data are sampled with high time-resolution, which are recorded in 5-minute intervals. With these features, the system is desirable for precise quantification of plant responses to stimuli at the whole-plant level. The numerous photosynthesis data produced by the photosynthesis monitoring system can be used for analysis and forecasting by way of modeling. Estimation models for the net photosynthetic rate (Pn) as a function of environmental factors for greenhouse tomato utilizing numerous data were limited to the whole greenhouse, such as the work of Nederhoff and Vegter (1994a). They used three variables of incident photosynthetically active radiation (PAR), CO2 concentration, with double rectangular hyperbolic relation and leaf area index (LAI) to calculate the net canopy photosynthesis rate of tomato greenhouse with an R of 0.892. In another work, they modified two established mechanical models of Acock (Acock et al., 1978) and Thornley (Thornley, 1976) to fit their data and gave Rs of 0.893 and 0.817, respectively (Nederhoff and Vegter, 1994b). On the other hand, the accuracy of sensor-based technology in modern greenhouses is jeopardized by disturbances, such as inaccuracy of the measurements by the sensor itself due to dynamic variations in the greenhouse climate (van Mourik et al., 2019), especially when measurements are performed in high time resolution. Therefore, it leads to an issue of how to address the high time-resolution data produced by the sensors for further use of model development. As most real-time data contain erroneous values and noise, such data required further processing for filtering noise and smoothing. Averaging techniques commonly used for smoothing data include the moving average (Čampulová, 2018) and the simple average (Yaffee and McGee, 2000). When the data are correctly prepared, the quality of the model can be reliable (Pyle, 1999). The objective of the present study was to process the","PeriodicalId":85505,"journal":{"name":"Seibutsu kankyo chosetsu. [Environment control in biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43197619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Future global temperature change, with predicted 1.5– 5.8 °C increases in temperatures by 2100, will cause increased heat stress to plants and create threats to agricultural production (Rosenzweig et al., 2001). The increasing threat of temperature change is already having a substantial impact on agricultural production worldwide as heat waves cause significant yield losses posing great risks for future food security for humankind (Christensen and Christensen, 2007). The unfavorable effects of heat stress can be mitigated by developing crop plants with improved thermotolerance using an assortment of genetic approaches. For this reason, it is crucial to have a thorough understanding of the physiological responses of plants to high temperatures and their mechanisms of heat tolerance, as well as to formulate possible strategies for improving crop thermotolerance. Photosynthesis is one of the most sensitive physiological responses in plants to heat stress. Thus, it is important to maintain high photosynthetic activity for heat stress tolerance in plants (Berry and Björkman, 1980). When plants are subjected to high temperatures, carbon dioxide (CO2) fixation, oxygen (O2) evolution, and photophosphorylation are restrained rapidly (Berry and Björkman, 1980). The limit of CO2 fixation by high temperature occurs simultaneously with the inactivation of ribulose 1, 5 bisphosphate (RuBP) carboxylase/oxygenase (RuBisCO) activase, which leads to the activation of RuBisCO (Feller et al., 1998; Salvucci et al., 2004). In the thylakoid membrane, the most sensitive component element to high temperature is photosystem II (PSII). Heat stress may suppress the light-absorption capacity of the plant owing to the dissolution of the O2 evolution apparatus (Mamedov et al., 1993; Nash, et al., 1985; Tompson et al., 1989). Many studies have shown that the instantaneous response of leaf carbon exchange to temperature depends on the temperature experienced by the plant over longer time periods, a response termed temperature acclimation (Atkin et al., 2005; Atkin and Tjoelker, 2003; Berry and Björkman, 1980; Smith and Dukes, 2013; Way and Yamori, 2014; Yamori et al., 2014). Temperature acclimation can be observed through a change in the parameters that define the instantaneous temperature response curve as a result of changes previously experienced by the plant or the acclimated temperature (Atkin and Tjoelker, 2003). Hikosaka et al. (2006) indicated that changes in the photosynthesis-temperature curve with long-term thermal acclimation are attributable to four factors: intercellular CO2 concentration, activation energy of the maximum rate of RuBP carboxylation (Vcmax), activation energy of the rate of RuBP regeneration (Jmax), and the ratio of Jmax to Vcmax. Of these, the activation energy of Vcmax may be the most important factor that influences thermal acclimation. Smith and Dukes (2017) also indicated that “fast mechanism” of thermal acclimation may be attributable to
{"title":"Short-term Thermal Acclimation Increases Ribulose 1, 5 Bisphosphate Carboxylase/Oxygenase Activity and Content and Enhances Heat Stress Tolerance of Photosynthesis in Cucumber","authors":"K. Nada, Yuuichi Nagaya, S. Hiratsuka","doi":"10.2525/ECB.59.69","DOIUrl":"https://doi.org/10.2525/ECB.59.69","url":null,"abstract":"Future global temperature change, with predicted 1.5– 5.8 °C increases in temperatures by 2100, will cause increased heat stress to plants and create threats to agricultural production (Rosenzweig et al., 2001). The increasing threat of temperature change is already having a substantial impact on agricultural production worldwide as heat waves cause significant yield losses posing great risks for future food security for humankind (Christensen and Christensen, 2007). The unfavorable effects of heat stress can be mitigated by developing crop plants with improved thermotolerance using an assortment of genetic approaches. For this reason, it is crucial to have a thorough understanding of the physiological responses of plants to high temperatures and their mechanisms of heat tolerance, as well as to formulate possible strategies for improving crop thermotolerance. Photosynthesis is one of the most sensitive physiological responses in plants to heat stress. Thus, it is important to maintain high photosynthetic activity for heat stress tolerance in plants (Berry and Björkman, 1980). When plants are subjected to high temperatures, carbon dioxide (CO2) fixation, oxygen (O2) evolution, and photophosphorylation are restrained rapidly (Berry and Björkman, 1980). The limit of CO2 fixation by high temperature occurs simultaneously with the inactivation of ribulose 1, 5 bisphosphate (RuBP) carboxylase/oxygenase (RuBisCO) activase, which leads to the activation of RuBisCO (Feller et al., 1998; Salvucci et al., 2004). In the thylakoid membrane, the most sensitive component element to high temperature is photosystem II (PSII). Heat stress may suppress the light-absorption capacity of the plant owing to the dissolution of the O2 evolution apparatus (Mamedov et al., 1993; Nash, et al., 1985; Tompson et al., 1989). Many studies have shown that the instantaneous response of leaf carbon exchange to temperature depends on the temperature experienced by the plant over longer time periods, a response termed temperature acclimation (Atkin et al., 2005; Atkin and Tjoelker, 2003; Berry and Björkman, 1980; Smith and Dukes, 2013; Way and Yamori, 2014; Yamori et al., 2014). Temperature acclimation can be observed through a change in the parameters that define the instantaneous temperature response curve as a result of changes previously experienced by the plant or the acclimated temperature (Atkin and Tjoelker, 2003). Hikosaka et al. (2006) indicated that changes in the photosynthesis-temperature curve with long-term thermal acclimation are attributable to four factors: intercellular CO2 concentration, activation energy of the maximum rate of RuBP carboxylation (Vcmax), activation energy of the rate of RuBP regeneration (Jmax), and the ratio of Jmax to Vcmax. Of these, the activation energy of Vcmax may be the most important factor that influences thermal acclimation. Smith and Dukes (2017) also indicated that “fast mechanism” of thermal acclimation may be attributable to","PeriodicalId":85505,"journal":{"name":"Seibutsu kankyo chosetsu. [Environment control in biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49642995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Real-time photosynthetic rate monitoring is crucial for managing crop cultivation in greenhouses. Nederhoff and Vegter (1994) accordingly presented a canopy photosynthesis measurement method that enabled the accurate estimation of the greenhouse CO2 balance. The photosynthesis of cultivated plants in a greenhouse is directly related to the ventilation rate, which also affects the air temperature and humidity. Takakura et al. (2017) proposed a method for directly estimating the canopy photosynthetic rate by introducing the ventilation rate, determined from the greenhouse environmental parameters, into the CO2 balance equation. The ventilation is very complex as it is the result of the heat transfer processes of conduction, convection, and radiation occurring in a naturally ventilated greenhouse. Additionally, the ventilation rate has been found to be influenced by the presence of crops as well as the structure and design of the greenhouse, and has been observed to constantly fluctuate throughout the day (Mashonjowa et al., 2010). Therefore, it is necessary to continuously measure the ventilation rate in greenhouses used for cultivation. Various ventilation rate measurement techniques have been studied extensively, such as the tracer gas (TG), heat balance (HB), and water vapor balance (WVB) methods. The TG and HB methods are the most widely adopted for greenhouse ventilation rate measurement (Fernandez and Bailey, 1992). In previous research, the TG method has exhibited highly accurate air exchange rate measurement under leakage conditions (i.e., with the window apertures closed) and with the smallest window apertures (Fernandez and Bailey, 1992; Nederhoff et al., 1985; Baptista et al., 1999; Muñoz et al., 1999). Other studies have shown that the HB method achieves high accuracy with larger window apertures (Fernandez and Bailey, 1992; Baptista et al., 2001). However, the WVB method was found to estimate the ventilation rate more accurately than the TG method with small window apertures (Boulard and Draoui, 1995) and has been applied in a greenhouse used to cultivate mature plants (Harmanto et al., 2006). It is important to note that the TG method is not suitable for long-term, continuous ventilation rate measurement (Sherman, 1990) because it requires that a considerable amount of the TG be present in a greenhouse under cultivation, and SF6, which is often used as a TG, is quite expensive. Meanwhile, the HB technique requires numerous variables to measure the ventilation rate even when it is possible to do so continuously (Baptista et al., 1999). There are also several challenges associated with the WVB method related to the i) direct measurement of the transpiration rate parameter using a lysimetric device (Kittas et al., 2002); ii) overestimation of the ventilation rate at night (Mashonjowa et al., 2010); and iii) evaluation of the error
{"title":"Comparison of Three Ventilation Rate Measurement Methods under Different Window Apertures in Winter and Spring","authors":"A. Tusi, T. Shimazu, M. Ochiai, Katsumi Suzuki","doi":"10.2525/ECB.59.49","DOIUrl":"https://doi.org/10.2525/ECB.59.49","url":null,"abstract":"Real-time photosynthetic rate monitoring is crucial for managing crop cultivation in greenhouses. Nederhoff and Vegter (1994) accordingly presented a canopy photosynthesis measurement method that enabled the accurate estimation of the greenhouse CO2 balance. The photosynthesis of cultivated plants in a greenhouse is directly related to the ventilation rate, which also affects the air temperature and humidity. Takakura et al. (2017) proposed a method for directly estimating the canopy photosynthetic rate by introducing the ventilation rate, determined from the greenhouse environmental parameters, into the CO2 balance equation. The ventilation is very complex as it is the result of the heat transfer processes of conduction, convection, and radiation occurring in a naturally ventilated greenhouse. Additionally, the ventilation rate has been found to be influenced by the presence of crops as well as the structure and design of the greenhouse, and has been observed to constantly fluctuate throughout the day (Mashonjowa et al., 2010). Therefore, it is necessary to continuously measure the ventilation rate in greenhouses used for cultivation. Various ventilation rate measurement techniques have been studied extensively, such as the tracer gas (TG), heat balance (HB), and water vapor balance (WVB) methods. The TG and HB methods are the most widely adopted for greenhouse ventilation rate measurement (Fernandez and Bailey, 1992). In previous research, the TG method has exhibited highly accurate air exchange rate measurement under leakage conditions (i.e., with the window apertures closed) and with the smallest window apertures (Fernandez and Bailey, 1992; Nederhoff et al., 1985; Baptista et al., 1999; Muñoz et al., 1999). Other studies have shown that the HB method achieves high accuracy with larger window apertures (Fernandez and Bailey, 1992; Baptista et al., 2001). However, the WVB method was found to estimate the ventilation rate more accurately than the TG method with small window apertures (Boulard and Draoui, 1995) and has been applied in a greenhouse used to cultivate mature plants (Harmanto et al., 2006). It is important to note that the TG method is not suitable for long-term, continuous ventilation rate measurement (Sherman, 1990) because it requires that a considerable amount of the TG be present in a greenhouse under cultivation, and SF6, which is often used as a TG, is quite expensive. Meanwhile, the HB technique requires numerous variables to measure the ventilation rate even when it is possible to do so continuously (Baptista et al., 1999). There are also several challenges associated with the WVB method related to the i) direct measurement of the transpiration rate parameter using a lysimetric device (Kittas et al., 2002); ii) overestimation of the ventilation rate at night (Mashonjowa et al., 2010); and iii) evaluation of the error","PeriodicalId":85505,"journal":{"name":"Seibutsu kankyo chosetsu. [Environment control in biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45505860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tomoe Iwao, Taku Murakami, Osamu Akaboshi, Hnin Yin Cho, M. Yamada, S. Takahashi, M. Kato, N. Horiuchi, I. Ogiwara
In Japan, June-bearing strawberry cultivars are commercially produced from November to May in forcing greenhouses (Yoshida and Nishimoto, 2020), and June to October is off-season due to their physiological characteristics (Yamasaki, 2013; Yamazaki and Yano, 2016). However, fresh strawberry fruits are in demand all year round for eating raw or topping cakes (Yanagi, 2017), and approximately, 3,000 tons are imported annually from the United States to fill the supply gap (Tokyo Customs, 2014; Ministry of Agriculture, Forestry and Fisheries, 2019). As the appearance and taste of the imported fruits are not highly appreciated in the Japanese market, in addition to concerns on pesticide application and post-harvest treatments abroad, domestically produced strawberries are preferred (Imada, 2007). Therefore, everbearing strawberries are planted in cold areas for off-seasons to increase the production of domestic strawberries in summer and autumn (Yamazaki, 2015; Ohta and Yasuba, 2019). On the other hand, as confectioneries claim that everbearing strawberries have a poorer taste than that of June-bearing strawberries (Shibuya, 2010; Hamano et al., 2020), they want a year-round stable supply of June-bearing strawberries. In the plant factory with artificial light (PFAL), leafy vegetables such as lettuce grow rapidly and yield a yearround high under low-intensity fluorescent and LED lights, with little to no pesticide application (Yoshida et al., 2016), and fresh leafy vegetable production in PFALs is increasing (Goto, 2012). However, fruits, including strawberries, require stronger light intensity to facilitate photosynthesis (Shimizu et al., 2011; Maeda et al., 2016; Furuyama et al., 2017). In addition, the running cost of PFALs for more than 2 months of nursery period will increase, with no strawberry production (Fushihara, 2005). The most important difference between fruit and leafy vegetable cultivation in PFALs is that, the former needs modified day length and temperature in each stage of development for fruit production (Sønsteby and Heide, 2006; Hytönen and Kurokura, 2020). Off-season strawberry production of June-bearing cultivars in PFALs has been studied in Japan (Suwa and Nakajima, 2014), and a patent has been applied for cultivation methods (Nisshinbo Holdings Co., Ltd., 2013). However, production of high-quality strawberries in PFALs is uncommon because of insufficient fruit yield to meet the operational costs (Yoshida et al., 2013). In recent years, PFALs have been developed for
在日本，六月结草莓品种在11月至5月的强制温室中进行商业生产（Yoshida和Nishimoto，2020），由于其生理特性，6月至10月是淡季（Yamasaki，2013；Yamazaki和Yano，2016）。然而，新鲜草莓水果全年都有需求，可用于生食或浇头蛋糕（Yanagi，2017），每年从美国进口约3000吨，以填补供应缺口（东京海关，2014；农林水产省，2019）。由于进口水果的外观和味道在日本市场上不太受欢迎，除了在国外使用杀虫剂和采后处理外，国内生产的草莓更受欢迎（Imada，2007）。因此，在寒冷地区的淡季种植常青草莓，以增加夏秋国产草莓的产量（Yamazaki，2015；Ohta和Yasuba，2019）。另一方面，由于糖果商声称，常结草莓的味道比六月结草莓差（Shibuya，2010；Hamano等人，2020），他们希望全年稳定供应六月结草莓。在有人造光的植物工厂中，生菜等叶菜在低强度荧光灯和LED灯下生长迅速，产量达到全年新高，几乎没有施用农药（Yoshida et al.，2016），而在人造光工厂中，新鲜叶菜的产量正在增加（Goto，2012）。然而，包括草莓在内的水果需要更强的光照强度来促进光合作用（Shimizu等人，2011；Maeda等人，2016；Furuyama等人，2017）。此外，由于没有草莓生产，PFAL在2个月以上的苗圃期内的运行成本将增加（Fushihara，2005）。PFAL中水果和叶菜种植之间最重要的区别是，前者在水果生产的每个发育阶段都需要改变日长和温度（Sønsteby和Heide，2006；Hytönen和Kurokura，2020）。日本研究了PFAL中June-bearing品种的非季节草莓生产（Suwa和Nakajima，2014），并申请了栽培方法专利（Nishinbo Holdings Co.，有限公司，2013）。然而，PFAL生产高质量草莓的情况并不常见，因为水果产量不足，无法满足运营成本（Yoshida等人，2013）。近年来，PFAL已被开发用于
{"title":"Possibility of Harvesting June-bearing Strawberries in a Plant Factory with Artificial Light during Summer and Autumn by Re-using Plants Cultivated by Forcing Culture","authors":"Tomoe Iwao, Taku Murakami, Osamu Akaboshi, Hnin Yin Cho, M. Yamada, S. Takahashi, M. Kato, N. Horiuchi, I. Ogiwara","doi":"10.2525/ECB.59.99","DOIUrl":"https://doi.org/10.2525/ECB.59.99","url":null,"abstract":"In Japan, June-bearing strawberry cultivars are commercially produced from November to May in forcing greenhouses (Yoshida and Nishimoto, 2020), and June to October is off-season due to their physiological characteristics (Yamasaki, 2013; Yamazaki and Yano, 2016). However, fresh strawberry fruits are in demand all year round for eating raw or topping cakes (Yanagi, 2017), and approximately, 3,000 tons are imported annually from the United States to fill the supply gap (Tokyo Customs, 2014; Ministry of Agriculture, Forestry and Fisheries, 2019). As the appearance and taste of the imported fruits are not highly appreciated in the Japanese market, in addition to concerns on pesticide application and post-harvest treatments abroad, domestically produced strawberries are preferred (Imada, 2007). Therefore, everbearing strawberries are planted in cold areas for off-seasons to increase the production of domestic strawberries in summer and autumn (Yamazaki, 2015; Ohta and Yasuba, 2019). On the other hand, as confectioneries claim that everbearing strawberries have a poorer taste than that of June-bearing strawberries (Shibuya, 2010; Hamano et al., 2020), they want a year-round stable supply of June-bearing strawberries. In the plant factory with artificial light (PFAL), leafy vegetables such as lettuce grow rapidly and yield a yearround high under low-intensity fluorescent and LED lights, with little to no pesticide application (Yoshida et al., 2016), and fresh leafy vegetable production in PFALs is increasing (Goto, 2012). However, fruits, including strawberries, require stronger light intensity to facilitate photosynthesis (Shimizu et al., 2011; Maeda et al., 2016; Furuyama et al., 2017). In addition, the running cost of PFALs for more than 2 months of nursery period will increase, with no strawberry production (Fushihara, 2005). The most important difference between fruit and leafy vegetable cultivation in PFALs is that, the former needs modified day length and temperature in each stage of development for fruit production (Sønsteby and Heide, 2006; Hytönen and Kurokura, 2020). Off-season strawberry production of June-bearing cultivars in PFALs has been studied in Japan (Suwa and Nakajima, 2014), and a patent has been applied for cultivation methods (Nisshinbo Holdings Co., Ltd., 2013). However, production of high-quality strawberries in PFALs is uncommon because of insufficient fruit yield to meet the operational costs (Yoshida et al., 2013). In recent years, PFALs have been developed for","PeriodicalId":85505,"journal":{"name":"Seibutsu kankyo chosetsu. [Environment control in biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48877110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. T. Nguyen, Ricardo Ospina, N. Noguchi, H. Okamoto, Quang Hieu Ngo
In rice cultivation, the majority of pesticides are used focusing on three main problems: herbicides for weed management, fungicides for disease management, and insecticides for pest management. The Food and Agricultural Organization reports ratios of pesticide application in rice fields in Vietnam on 2002 of 25.3% herbicides, 32.6% fungicides, and 40.3% insecticides (FAO, 2005). Fungal leaf blast (LB) disease and bacterial leaf blight (BB) disease are common and well-known diseases found in the rice fields of Vietnam. The rice blast fungus Pyricularia oryzae causes a fungal disease common in rice fields (Francisco and Zahirul, 2003). Depending on the site of the symptoms, the rice blast disease is referred to as leaf blast, collar blast, node blast or neck blast. In the early stages of LB, the lesions on the leaf blade are elliptical or spindle-shaped, with brown borders and gray centers, as shown in Fig. 1a. The bacterium Xanthomonas oryzae causes a bacterial disease (Francisco and Zahirul, 2003) characterized by a water-soaked lesion that usually starts at the leaf margins, a few centimeters away from the tip, and spreads towards the leaf base. The affected areas increase in length and width, and become yellowish to light brown due to dryness, with a yellowish border between dead and green areas of the leaf, as shown in Fig. 1b. Several studies have reported that LB and BB are the most harmful rice diseases and have caused yield losses. For example, rice disease resulted in a yield reduction of 1– 10% from 456 farmer’s fields surveyed across tropical Asia on during 1987–1997 (Savary et al., 2000), and rice yield losses ranging from 50% to 85% have been reported in the Philippines by the International Rice Research Institute (IRRI, 2020). In Vietnam, grain yield losses of 38.21% to 64.57% due to neck blast disease have been reported (Hai et al., 2007). Thus, plant protection focusing on managing diseases and controlling the amount of fungicides to be applied has been an important part of research over the last few years. The literature includes many reports on the detection of rice diseases. Ks and Sahayadhas (2018) report on the prediction of early symptoms of BB and brown spots on rice plants by separating leaf color, signs and illumination from different color channels. This algorithm makes it easy to perform final feature analyses, however, it cannot predict diseases with symptoms in similar colors. Bakar et al. (2018) describe an integrated method for the detection of LB using three categories: infection stage, spreading stage, and worst stage. This is possible by analyzing the Hue, Saturation and Value color spaces with multi-level thresholding, and identifying classified regions of interest during image segmentation. This technique successfully detects the disease based on images taken in uncontrolled environments, however, it is not suitable for the detection of other diseases with similar features. In another study, Islam et al. (2018)
在水稻种植中，大多数杀虫剂的使用集中在三个主要问题上：用于杂草管理的除草剂、用于疾病管理的杀菌剂和用于害虫管理的杀虫剂。粮食及农业组织报告，2002年越南稻田的农药施用比例为25.3%的除草剂、32.6%的杀菌剂和40.3%的杀虫剂（粮农组织，2005年）。真菌性叶瘟病（LB）和细菌性叶枯病（BB）是在越南稻田中发现的常见和众所周知的疾病。稻瘟病真菌稻瘟病菌引起一种常见于稻田的真菌病（Francisco和Zahirul，2003）。根据症状的部位，稻瘟病被称为叶瘟病、颈瘟病、节瘟病或颈瘟病。在LB的早期阶段，叶片上的病变呈椭圆形或纺锤形，边界为棕色，中心为灰色，如图所示。第1a段。水稻黄单胞菌引起一种细菌性疾病（Francisco和Zahirul，2003），其特征是通常从叶缘开始，距离叶尖几厘米，并向叶基部扩散。受影响的区域长度和宽度增加，由于干燥而变为淡黄色至浅棕色，叶片的死亡区域和绿色区域之间有黄色边界，如图所示。1b。几项研究表明，LB和BB是危害最大的水稻病害，并造成了产量损失。例如，1987年至1997年期间，在亚洲热带地区调查的456块农田中，水稻病害导致产量下降了1–10%（Savary et al.，2000），国际水稻研究所（IRRI，2020）报告称，菲律宾的水稻产量损失在50%至85%之间。据报道，在越南，由于颈瘟，粮食产量损失了38.21%至64.57%（Hai等人，2007年）。因此，过去几年来，植物保护一直是研究的重要组成部分，其重点是控制疾病和控制杀菌剂的用量。文献包括许多关于水稻病害检测的报道。Ks和Sahayadhas（2018）报道了通过分离不同颜色通道的叶片颜色、体征和光照来预测水稻植株BB和褐色斑点的早期症状。该算法可以很容易地进行最终特征分析，但无法预测症状相似的疾病。Bakar等人（2018）描述了一种使用三类检测LB的综合方法：感染阶段、传播阶段和最差阶段。这可以通过使用多级阈值分析色调、饱和度和值颜色空间，并在图像分割过程中识别感兴趣的分类区域来实现。该技术基于在不受控制的环境中拍摄的图像成功地检测到了疾病，然而，它不适用于检测具有类似特征的其他疾病。在另一项研究中，Islam等人（2018）提出了高斯-朴素贝叶斯方法，使用图像处理基于受影响部分RGB值的百分比对疾病进行分类。该方法已成功检测出水稻褐斑病、白叶枯病和稻瘟病三种病害，处理时间快，准确率高，但无法检测出颜色特征相似但形状不同的病害。
{"title":"Real-time Disease Detection in Rice Fields in the Vietnamese Mekong Delta","authors":"T. T. Nguyen, Ricardo Ospina, N. Noguchi, H. Okamoto, Quang Hieu Ngo","doi":"10.2525/ECB.59.77","DOIUrl":"https://doi.org/10.2525/ECB.59.77","url":null,"abstract":"In rice cultivation, the majority of pesticides are used focusing on three main problems: herbicides for weed management, fungicides for disease management, and insecticides for pest management. The Food and Agricultural Organization reports ratios of pesticide application in rice fields in Vietnam on 2002 of 25.3% herbicides, 32.6% fungicides, and 40.3% insecticides (FAO, 2005). Fungal leaf blast (LB) disease and bacterial leaf blight (BB) disease are common and well-known diseases found in the rice fields of Vietnam. The rice blast fungus Pyricularia oryzae causes a fungal disease common in rice fields (Francisco and Zahirul, 2003). Depending on the site of the symptoms, the rice blast disease is referred to as leaf blast, collar blast, node blast or neck blast. In the early stages of LB, the lesions on the leaf blade are elliptical or spindle-shaped, with brown borders and gray centers, as shown in Fig. 1a. The bacterium Xanthomonas oryzae causes a bacterial disease (Francisco and Zahirul, 2003) characterized by a water-soaked lesion that usually starts at the leaf margins, a few centimeters away from the tip, and spreads towards the leaf base. The affected areas increase in length and width, and become yellowish to light brown due to dryness, with a yellowish border between dead and green areas of the leaf, as shown in Fig. 1b. Several studies have reported that LB and BB are the most harmful rice diseases and have caused yield losses. For example, rice disease resulted in a yield reduction of 1– 10% from 456 farmer’s fields surveyed across tropical Asia on during 1987–1997 (Savary et al., 2000), and rice yield losses ranging from 50% to 85% have been reported in the Philippines by the International Rice Research Institute (IRRI, 2020). In Vietnam, grain yield losses of 38.21% to 64.57% due to neck blast disease have been reported (Hai et al., 2007). Thus, plant protection focusing on managing diseases and controlling the amount of fungicides to be applied has been an important part of research over the last few years. The literature includes many reports on the detection of rice diseases. Ks and Sahayadhas (2018) report on the prediction of early symptoms of BB and brown spots on rice plants by separating leaf color, signs and illumination from different color channels. This algorithm makes it easy to perform final feature analyses, however, it cannot predict diseases with symptoms in similar colors. Bakar et al. (2018) describe an integrated method for the detection of LB using three categories: infection stage, spreading stage, and worst stage. This is possible by analyzing the Hue, Saturation and Value color spaces with multi-level thresholding, and identifying classified regions of interest during image segmentation. This technique successfully detects the disease based on images taken in uncontrolled environments, however, it is not suitable for the detection of other diseases with similar features. In another study, Islam et al. (2018)","PeriodicalId":85505,"journal":{"name":"Seibutsu kankyo chosetsu. [Environment control in biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47338071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Noriko Ohtake, Yao Ju, M. Ishikura, H. Suzuki, Shunsuke Adachi, W. Yamori
Cultivation in a closed-type plant factory with artificial lighting enables year-round production of crops with a stable yield and uniform quality. This is in contrast to cultivation in fields and in sunlight-type plant factories where environmental fluctuations can reduce crop yield and quality. Therefore, closed-type plant factories are expected to be applicable to harsh environments inadequate for crop production and to lead to improved global food security (Kozai, 2013; Anpo et al., 2019). However, the high electricity cost due to the artificial lighting diminishes the benefit of improved sales, which hinders new entry into the business (Kozai and Niu, 2020). Identifying optimal irradiation methods to maximize crop yield without increasing electricity costs could enhance the benefits of closed-type plant factories and lead to an expansion in operations. In most cases, artificial red (R) and blue (B) light from light-emitting diodes (LEDs) is used for cultivation in plant factories since these wavelengths are specifically absorbed by chlorophyll to drive photosynthetic processes (Pfündel and Baake, 1990; Massa et al., 2008). In addition, monochromatic R or B light alone is unsuitable for crop production, because compared to simultaneous R+B (RB) light or white (W) light, R light alone decreases photosynthetic rate and biomass, and leads to abnormal shape (Goins et al., 1998; Wang et al., 2015), and B light alone decreases stem length, leaf area, and photosynthetic rate due to chloroplast avoidance response (Wada et al., 2003; Kim et al., 2004). It is widely recognized that simultaneous RB light is a promising irradiation procedure for vegetable plants including pepper (Piper nigrum), lettuce (Lactuca sativa L.), spinach (Spinacia oleracea), radish (Raphanus sativus var. sativus), tomato (Solanum lycopersicum), rapeseed (Brassica napus), and cucumber (Cucumis sativus L.) (Brown et al., 1995; Yorio et al., 2001; Nanya et al., 2012; Li et al., 2013; Miao et al., 2019). Previous studies have attempted to find optimal controls of R and B light, including intensity of photosynthetic photon flux density (PPFD) (Yanagi et al., 1996; Zha and Liu, 2018), length of photoperiod (Jao and Fang, 2004; Jishi et al., 2016), and ratio of R light to B light (Okamoto et al., 1997; Hogewoning et al., 2010; Borowski et al., 2015; Wang et al., 2016). Recent studies also report that the patterns of R and B light irradiation affect plant growth. For instance, Shimokawa et al. (2014) found that alternating irradiation with R and B LEDs (12 hours R : 12 hours B) enhanced growth in leafy lettuce (Lactuca sativa L. cv. ‘Summer Surge’), compared with lettuce grown under W light or simultaneous RB light (12 hours light : 12 hours dark) with the same daily light integrals. This phenomenon cannot be explained by a difference in day length, because alternating irradiation of red and blue (R/B) light also promoted lettuce
在人工照明的封闭式植物工厂中种植，可以全年生产产量稳定、质量均匀的作物。这与田间种植和日光型植物工厂形成鲜明对比，后者的环境波动会降低作物产量和质量。因此，封闭式植物工厂有望适用于不适合作物生产的恶劣环境，并改善全球粮食安全(Kozai, 2013;Anpo et al.， 2019)。然而，由于人工照明造成的高电力成本降低了改善销售的好处，这阻碍了新进入该业务(Kozai和Niu, 2020)。确定在不增加电力成本的情况下使作物产量最大化的最佳辐照方法，可以提高封闭式植物工厂的效益，并导致业务的扩大。在大多数情况下，来自发光二极管(led)的人造红光(R)和蓝光(B)光用于植物工厂的栽培，因为这些波长被叶绿素特异性地吸收以驱动光合作用过程(pf ndel和Baake, 1990;Massa et al.， 2008)。此外，单色R光或单色B光不适合作物生产，因为与同时使用R+B (RB)光或白光(W)光相比，单色R光会降低光合速率和生物量，并导致形状异常(Goins et al.， 1998;Wang et al.， 2015)，并且由于叶绿体回避反应，单独的B光会降低茎长、叶面积和光合速率(Wada et al.， 2003;Kim et al.， 2004)。人们普遍认为，同步RB光是一种很有前途的蔬菜照射方法，包括辣椒(Piper nigrum)、生菜(Lactuca sativa L.)、菠菜(Spinacia oleracea)、萝卜(Raphanus sativus var. sativus)、番茄(Solanum lycopersicum)、油菜籽(Brassica napus)和黄瓜(Cucumis sativus L.) (Brown et al.， 1995;Yorio et al.， 2001;南亚等，2012;Li et al.， 2013;Miao et al.， 2019)。先前的研究试图找到R光和B光的最佳控制，包括光合光子通量密度(PPFD)的强度(Yanagi等，1996;赵和刘，2018)，光周期长度(饶和方，2004;Jishi et al.， 2016)，以及R光与B光的比例(Okamoto et al.， 1997;Hogewoning等，2010;Borowski et al.， 2015;Wang等人，2016)。最近的研究也报告了R光和B光照射模式对植物生长的影响。例如，Shimokawa等人(2014)发现R和B led交替照射(12小时R: 12小时B)促进了叶莴苣(Lactuca sativa L. cv.)的生长。与在W光或同时RB光(12小时光照:12小时黑暗)下生长的生菜相比，它们的日光照积分相同。这种现象不能用白昼长度的差异来解释，因为红蓝光(R/B)交替照射也促进了生菜的生长
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