多地点试验中水稻作物对地点变异性的反应:对具体地点管理的呼吁

D. Rao, K. Surekha, Aruna L
{"title":"多地点试验中水稻作物对地点变异性的反应:对具体地点管理的呼吁","authors":"D. Rao, K. Surekha, Aruna L","doi":"10.35709/ory.2021.58.4.6","DOIUrl":null,"url":null,"abstract":"Yield is a net expression of genotype (G) x environment (E) interactions including management. However, the segregation of 'E' into respective causes is seldom done while 'G' is a constant. Soil is a component of 'E' with imminent variability in attributes among multiple locations. Data on yield response of varieties to a set of treatments in different soils from multi-locational yield maximisation trial under All India Coordinated Rice Improvement Project were regularly gathered. A dataset pertaining to a trial conducted in Karaikal district of Puducherry Union Territory was analysed to ascertain the site-specific crop responses with inherent variability in soils. \nRice varieties, ADT 46, BPT 5204 and CR 1009 were tested for responses at 17 sites with farmer fertiliser practices (FFP), regional recommended fertiliser dose (RDF) and software, 'Nutrient Expert®' (2016) (NE) derived fertiliser quantities. Analysis of variance showed that test sites explained 59.3% variability in yield. A multivariate technique, Factor Analysis extracted two factors, which are linear combinations of soil attributes those explained 76% of variance in soils. Factor scores classified soils into four groups, owing to variability in soil properties. Soil texture influenced yield significantly (across varieties and treatments) (R2 = 11.1%). Sites varied in excess duration in nursery ranging from 2 - 26 days. However, this excess duration reduced number of panicles m-2 only in CR 1009 (r = -0.328**). \nGeneral linear model with sites and treatments as fixed factors, their interactions and panicles m-2 as covariate predicted better (R2 = 90.3%) with their significant contribution to the model. The order of R2 (%) was Sites (59.3) > Varieties (27.4) > Treatments (13.6%) in explaining variability in yield highlighting site-specific responses. Mean differences between ADT 46 and BPT 5204; BPT 5204 and CR 1009 were significant. Yield significantly changed across sites and treatments when fertiliser management shifted from non-specific (FFP) to site-specific NE based calculations through RDF (region specific). Results of this trial placed emphasis on soil test-based crop management to realise the uniform best, which clearly is site specific crop management.","PeriodicalId":19618,"journal":{"name":"ORYZA- An International Journal on Rice","volume":"48 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rice crop response to site variability in a multi-locational trial: A call for site specific management\",\"authors\":\"D. Rao, K. Surekha, Aruna L\",\"doi\":\"10.35709/ory.2021.58.4.6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Yield is a net expression of genotype (G) x environment (E) interactions including management. However, the segregation of 'E' into respective causes is seldom done while 'G' is a constant. Soil is a component of 'E' with imminent variability in attributes among multiple locations. Data on yield response of varieties to a set of treatments in different soils from multi-locational yield maximisation trial under All India Coordinated Rice Improvement Project were regularly gathered. A dataset pertaining to a trial conducted in Karaikal district of Puducherry Union Territory was analysed to ascertain the site-specific crop responses with inherent variability in soils. \\nRice varieties, ADT 46, BPT 5204 and CR 1009 were tested for responses at 17 sites with farmer fertiliser practices (FFP), regional recommended fertiliser dose (RDF) and software, 'Nutrient Expert®' (2016) (NE) derived fertiliser quantities. Analysis of variance showed that test sites explained 59.3% variability in yield. A multivariate technique, Factor Analysis extracted two factors, which are linear combinations of soil attributes those explained 76% of variance in soils. Factor scores classified soils into four groups, owing to variability in soil properties. Soil texture influenced yield significantly (across varieties and treatments) (R2 = 11.1%). Sites varied in excess duration in nursery ranging from 2 - 26 days. However, this excess duration reduced number of panicles m-2 only in CR 1009 (r = -0.328**). \\nGeneral linear model with sites and treatments as fixed factors, their interactions and panicles m-2 as covariate predicted better (R2 = 90.3%) with their significant contribution to the model. The order of R2 (%) was Sites (59.3) > Varieties (27.4) > Treatments (13.6%) in explaining variability in yield highlighting site-specific responses. Mean differences between ADT 46 and BPT 5204; BPT 5204 and CR 1009 were significant. Yield significantly changed across sites and treatments when fertiliser management shifted from non-specific (FFP) to site-specific NE based calculations through RDF (region specific). Results of this trial placed emphasis on soil test-based crop management to realise the uniform best, which clearly is site specific crop management.\",\"PeriodicalId\":19618,\"journal\":{\"name\":\"ORYZA- An International Journal on Rice\",\"volume\":\"48 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ORYZA- An International Journal on Rice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35709/ory.2021.58.4.6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ORYZA- An International Journal on Rice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35709/ory.2021.58.4.6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

产量是基因型(G) x环境(E)相互作用(包括管理)的净表达。然而,当“G”为常数时,很少将“E”分离为各自的原因。土壤是“E”的一个组成部分,在多个地点之间具有即将发生的属性变化。定期收集全印度协调水稻改良项目多地点产量最大化试验中不同土壤中不同品种对一系列处理的产量响应数据。分析了与在普杜切里联合领土Karaikal地区进行的试验有关的数据集,以确定具有土壤固有变异性的特定地点作物反应。水稻品种ADT 46、BPT 5204和cr1009在17个地点进行了农民施肥实践(FFP)、区域推荐施肥剂量(RDF)和“营养专家”(2016)(NE)衍生肥料量软件的响应测试。方差分析表明,试验点解释了59.3%的产量变异。一种多变量技术,因子分析提取了两个因子,这两个因子是土壤属性的线性组合,解释了76%的土壤方差。由于土壤性质的可变性,因子得分将土壤分为四组。土壤质地对产量影响显著(R2 = 11.1%)。苗圃的超期时间各有不同,从2天到26天不等。然而,这一过量持续时间仅在cr1009中降低了穗数m-2 (r = -0.328**)。以场地和处理为固定因子,以它们的相互作用和穗粒m-2为协变量的一般线性模型预测效果较好(R2 = 90.3%),对模型的贡献显著。R2(%)的排序为:位点(59.3)>品种(27.4)>处理(13.6%)。ADT 46与BPT 5204的平均差异;BPT 5204和cr1009有显著性差异。当肥料管理从非特异性(FFP)转变为通过RDF(区域特异性)基于特定地点NE的计算时,不同地点和处理的产量发生了显著变化。本试验结果强调以土壤试验为基础的作物管理,以实现统一最佳,这显然是因地制宜的作物管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rice crop response to site variability in a multi-locational trial: A call for site specific management
Yield is a net expression of genotype (G) x environment (E) interactions including management. However, the segregation of 'E' into respective causes is seldom done while 'G' is a constant. Soil is a component of 'E' with imminent variability in attributes among multiple locations. Data on yield response of varieties to a set of treatments in different soils from multi-locational yield maximisation trial under All India Coordinated Rice Improvement Project were regularly gathered. A dataset pertaining to a trial conducted in Karaikal district of Puducherry Union Territory was analysed to ascertain the site-specific crop responses with inherent variability in soils. Rice varieties, ADT 46, BPT 5204 and CR 1009 were tested for responses at 17 sites with farmer fertiliser practices (FFP), regional recommended fertiliser dose (RDF) and software, 'Nutrient Expert®' (2016) (NE) derived fertiliser quantities. Analysis of variance showed that test sites explained 59.3% variability in yield. A multivariate technique, Factor Analysis extracted two factors, which are linear combinations of soil attributes those explained 76% of variance in soils. Factor scores classified soils into four groups, owing to variability in soil properties. Soil texture influenced yield significantly (across varieties and treatments) (R2 = 11.1%). Sites varied in excess duration in nursery ranging from 2 - 26 days. However, this excess duration reduced number of panicles m-2 only in CR 1009 (r = -0.328**). General linear model with sites and treatments as fixed factors, their interactions and panicles m-2 as covariate predicted better (R2 = 90.3%) with their significant contribution to the model. The order of R2 (%) was Sites (59.3) > Varieties (27.4) > Treatments (13.6%) in explaining variability in yield highlighting site-specific responses. Mean differences between ADT 46 and BPT 5204; BPT 5204 and CR 1009 were significant. Yield significantly changed across sites and treatments when fertiliser management shifted from non-specific (FFP) to site-specific NE based calculations through RDF (region specific). Results of this trial placed emphasis on soil test-based crop management to realise the uniform best, which clearly is site specific crop management.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信