Rangeland Management, College of Natural Resources, University of Tehran, Tehran, Iran.
Faculty of Natural Environment and Biodiversity Department, College of Environment, Standard Square, Karaj, Iran.
BMC Ecol. 2020 Aug 29;20(1):48. doi: 10.1186/s12898-020-00316-4.
Salvia is a large, diverse, and polymorphous genus of the family Lamiaceae, comprising about 900 ornamentals, medicinal species with almost cosmopolitan distribution in the world. The success of Salvia limbata seed germination depends on a numerous ecological factors and stresses. We aimed to analyze Salvia limbata seed germination under four ecological stresses of salinity, drought, temperature and pH, with application of artificial intelligence modeling techniques such as MLR (Multiple Linear Regression), and MLP (Multi-Layer Perceptron). The S.limbata seeds germination was tested in different combinations of abiotic conditions. Five different temperatures of 10, 15, 20, 25 and 30 °C, seven drought treatments of 0, -2, -4, -6, -8, -10 and -12 bars, eight treatments of salinity containing 0, 50, 100.150, 200, 250, 300 and 350 mM of NaCl, and six pH treatments of 4, 5, 6, 7, 8 and 9 were tested. Indeed 228 combinations were tested to determine the percentage of germination for model development.
Comparing to the MLR, the MLP model represents the significant value of R in training (0.95), validation (0.92) and test data sets (0.93). According to the results of sensitivity analysis, the values of drought, salinity, pH and temperature are respectively known as the most significant variables influencing S. limbata seed germination. Areas with high moisture content and low salinity in the soil have a high potential to seed germination of S. limbata. Also, the temperature of 18.3 °C and pH of 7.7 are proposed for achieving the maximum number of germinated S. limbata seeds.
Multilayer perceptron model helps managers to determine the success of S.limbata seed planting in agricultural or natural ecosystems. The designed graphical user interface is an environmental decision support system tool for agriculture or rangeland managers to predict the success of S.limbata seed germination (percentage) in different ecological constraints of lands.
鼠尾草是唇形科的一个大、多样和多态的属,包含约 900 种观赏植物和药用植物,几乎在全球范围内分布。齿叶鼠尾草种子的萌发成功取决于许多生态因素和胁迫。我们的目的是在盐度、干旱、温度和 pH 值等四种生态胁迫下分析齿叶鼠尾草种子的萌发情况,并应用人工智 能建模技术,如多元线性回归(MLR)和多层感知器(MLP)。测试了齿叶鼠尾草种子在不同非生物条件下的萌发情况。测试了 5 种不同温度(10、15、20、25 和 30°C)、7 种不同干旱处理(0、-2、-4、-6、-8、-10 和-12 巴)、8 种含有 0、50、100、150、200、250、300 和 350mM NaCl 的盐度处理以及 6 种 pH 值处理(4、5、6、7、8 和 9)。实际上,测试了 228 种组合来确定模型开发的萌发百分比。
与 MLR 相比,MLP 模型在训练(0.95)、验证(0.92)和测试数据集(0.93)中代表 R 的显著值。根据敏感性分析的结果,干旱、盐度、pH 值和温度值分别被认为是影响齿叶鼠尾草种子萌发的最重要变量。土壤中水分含量高、盐度低的地区,齿叶鼠尾草种子萌发的潜力较大。此外,18.3°C 的温度和 7.7 的 pH 值被提议用于获得最大数量的萌发的齿叶鼠尾草种子。
多层感知器模型有助于管理者确定农业或自然生态系统中齿叶鼠尾草种子种植的成功。设计的图形用户界面是农业或牧场管理者的环境决策支持系统工具,用于预测不同土地生态约束下齿叶鼠尾草种子萌发(百分比)的成功。