Non-destructive Prediction Models for Estimation of Leaf Area for Most Commonly Grown Vegetable Crops in Ethiopia

M. Yeshitila, M. Taye
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引用次数: 10

Abstract

Leaf area (LA) is a valuable key for evaluating plant growth, therefore rapid, accurate, simple, and nondestructive methods for LA determination are important for physiological and agronomic studies. The objective of this study was to develop a model for leaf area prediction from simple non-destructive measurements in some most commonly cultivated vegetable crops’ accessions in the country. A field experiment was carried out from May to August of 2014 at ‘Hawassa College of Agriculture’s research site, using ten selected most commonly grown vegetable species of Potato (Solanum tuberosum. L), Cabbage (Brassica campestris L.), Pepper (Capsicum annuum L.), Beetroot (Beta vulgaris), Swisschard (Beta vulgaris), Sweet potato (Ipomoea batatas L.), Snapbean (Vicia Snap L.) and Onion (Allium cepa). A standard method (LICOR LI-3000C) was also used for measuring the actual areas of the leaves. All equations produced for leaf area were derived as affected by leaf length and leaf width. As a result of ANOVA and multiple-regression analysis, it was found that there was close relationship between actual and predicted growth parameters. The produced leaf area prediction models in the present study are: AREA (cm2) = -16.882+2.533L (cm) + 4.5076W (cm) for Pepper Melka Awaze Variety. AREA (cm2) = -18.943+2.225L (cm) + 5.710W (cm) for Pepper Melka Zale Variety. AREA (cm2) = 136.8524 + 2.68L (cm) + 2.564W (cm) for Sweet-potato. AREA (cm2) = -193.518 + 8.633L (cm) + 14.018W (cm) for Beetroot. AREA (cm2) = -23.1534 + 1.1023L (cm) + 16.156W (cm) for Onion. AREA (cm2) = -260.265 + 27.115 (L (cm) * W (cm)) for Cabbage. AREA (cm2) = -422.973 + 22.752L (cm) + 8.31W (cm) for Swisschard. AREA (cm2) = 68.85 – 13.47L (cm) + 7.34W + 0.645L2 (cm) -0.012W2 (cm) for Snapbean. R2 values (0.989, 0.976, 0.917, 0.926, 0.924, 0.966, 0.917, and 0.966 for the pepper Melka Awaze Variety, Pepper Melka Zale Variety, Sweetpotato, Beetroot, Onion, Cabbage, Swisschard and Snapbean respectively) and standard errors for all subsets of the independent variables were found to be significant at the p<0.001 level.
埃塞俄比亚最常见蔬菜作物叶面积的非破坏性预测模型
叶面积(LA)是评价植物生长的重要指标,因此快速、准确、简单、无损的测定方法对生理和农艺研究具有重要意义。本研究的目的是建立一个基于简单无损测量的叶面积预测模型,用于我国一些最常见的蔬菜作物材料的叶面积预测。2014年5月至8月,在哈瓦萨农业学院的研究基地进行了一项田间试验,选择了10种最常见的蔬菜马铃薯(Solanum tuberosum)。L)、白菜(Brassica campestris L.)、辣椒(Capsicum annuum L.)、甜菜根(Beta vulgaris)、瑞士菜(Beta vulgaris)、甘薯(Ipomoea batatas L.)、蚕豆(Vicia Snap L.)和洋葱(Allium cepa)。用标准方法(LICOR LI-3000C)测量叶片的实际面积。所有叶面积方程均受叶长和叶宽的影响。方差分析和多元回归分析发现,实际增长参数与预测增长参数之间存在密切的关系。本研究建立的叶面积预测模型为:甜椒品种叶面积(cm2) = -16.882+2.533L (cm) + 4.5076W (cm)。Melka Zale辣椒品种面积(cm2) = -18.943+2.225L (cm) + 5.710W (cm)红薯面积(平方厘米)= 136.8524 + 2.68L(厘米)+ 2.564W(厘米)。甜菜根面积(cm2) = -193.518 + 8.633L (cm) + 14.018W (cm)面积(cm2) = -23.1534 + 1.1023L (cm) + 16.156W (cm)面积(cm2) = -260.265 + 27.115 (L (cm) * W (cm))面积(cm2) = -422.973 + 22.752L (cm) + 8.31W (cm)。面积(cm2) = 68.85 - 13.47L (cm) + 7.34W + 0.645L2 (cm) -0.012 w2 (cm)各自变量亚集的R2值(分别为0.989、0.976、0.917、0.926、0.924、0.966、0.917和0.966)和标准误差在p<0.001水平上均显著。
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