Tolerance of forage pea cultivars to salinity and drought stress during germination and seedling growth

Küçüközcü, Gülçin; Avci, Süleyman

doi:10.31015/jaefs.2020.3.17

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…According to the results, it was predicted that ML algorithms at 9 degrees or less according to the SL parameter, 1 degree or less according to the RL parameter, 5 degrees or less according to the FW parameter, and 1 degree or less according to the DW parameter may result in the failure of fodder pea plants to germinate. When the temperature drops below the ideal range for germination, fodder pea plants either do not germinate at all or have a very poor germination rate [3,45].…”

Section: Discussionmentioning

confidence: 99%

“…Simultaneously, it has a detrimental impact on the growth and development of plants, thereby impairing their output [2]. Plants are susceptible to salinity at any stage of their growth cycle, beginning from the point when the seeds are deposited into the soil [3,4]. Nevertheless, plants exhibit heightened sensitivity, particularly throughout the stages of germination and seedling growth [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Using Machine Learning Algorithms to Investigate the Impact of Temperature Treatment and Salt Stress on Four Forage Peas (Pisum sativum var. arvense L.)

Okumuş,

Say,

Eren

et al. 2024

Horticulturae

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The combination of high or low temperatures and high salt may cause significant harm to the yield, quality, and overall productivity of forage pea crops. The germination process, a crucial phase in the life cycle of forage peas, may be greatly influenced by varying temperature and salinity conditions. To comprehend the influence of these elements on the germination of forage peas, one must use many tactics, including the choice of resilient forage pea cultivars. The experiment aimed to evaluate the response of four forage pea cultivars (Arda, Ozkaynak, Taskent, and Tore) caused by various temperature (10 °C, 15 °C, and 20 °C) and salt (0, 5, 10, 15, and 20 dS m−1) conditions at the germination stage using multivariate analysis and machine learning methods. An observation of statistical significance (p < 0.01) was made regarding the variations between genotypes, temperature–salt levels, and the interaction of the observed factors: germination percentage (GP), shoot length (SL), root length (RL), fresh weight (FW), and dry weight (DW). The cultivar Tore had the best values for SL (1.63 cm), RL (5.38 cm), FW (1.10 g), and DW (0.13 g) among all the cultivars. On the other hand, the Ozkaynak cultivar had the highest value for GP (89.13%). The values of all of the parameters that were investigated decreased as the salt level rose, whereas the values increased when the temperature level increased. As a result, the Tore cultivar exhibited the highest values for shoot length, root length, fresh weight, and dry weight variables when exposed to a maximum temperature of 20 °C and a saline level of 0 dS m−1. It was determined that temperature treatment of fodder peas can reduce salt stress if kept at optimum levels. The effects of temperature and salt treatments on the germination data of several fodder pea cultivars were analyzed and predicted. Three distinct machine learning algorithms were used to create predictions. Based on R2 (0.899), MSE (5.344), MAPE (6.953), and MAD (4.125) measures, the MARS model predicted germination power (GP) better. The GPC model performed better in predicting shoot length (R2 = 0.922, MSE = 0.602, MAPE = 11.850, and MAD = 0.326) and root length (R2 = 0.900, MSE = 0.719, MAPE = 12.673, and MAD = 0.554), whereas the Xgboost model performed better in estimating fresh weight (R2 = 0.966, MSE = 0.130, MAPE = 11.635, and MAD = 0.090) and dry weight (R2 = 0.895, MSE = 0.021, MAPE = 12.395, and MAD = 0.013). The results of the research show that the techniques and analyses used can estimate stress tolerance, susceptibility levels, and other plant parameters, making it a cost-effective and reliable way to quickly and accurately study forage peas and related species.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms to Investigate the Impact of Temperature Treatment and Salt Stress on Four Forage Peas (Pisum sativum var. arvense L.)

Okumuş,

Say,

Eren

et al. 2024

Horticulturae

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Developing and modeling precipitation duration curves and determining spatial and temporal distributions of precipitation over different percentages of time

Keskiner,

Çetin

2024

int. j. agric. environ. food sci.

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Determining the temporal distribution of precipitation is of critical importance for engineering hydrology, agricultural yield forecasting, and drought monitoring. The aims of the research carried out in this context were threefold: a) to develop "precipitation duration curves", i.e., PDCs, of long-term total precipitation series, b) to investigate likely mathematical models of PDCs of each meteorological station studied, and c) to determine spatial and temporal distributions of precipitation that occur equalled or exceeded at 50% and 80% of the time. In line with the objectives, the PDCs were developed for each meteorological station. To this end, long-term annual precipitation data series were obtained from a total of 11 meteorological stations located in and around the borders of Sanliurfa province. PDCs of meteorological stations were modelled using the fifth-order regression equation at the 5% significance level. The mathematical forms of the developed model equations were used to predict precipitation amounts for each station at 50% and 80% of the time. The predicted precipitation data were mapped to delineate the spatial distribution of precipitation, and then hypsometric curves were generated from these maps. It was found that the standard errors (SE) of the "precipitation duration curves" models showed an increasing tendency as the standard deviation of the rainfall series increased. Regression analysis results showed that the SE values of the models change in direct proportion to the increase in extreme precipitation values. Considering the amount of precipitation that exceeds or equals 50 per cent of the time, it can be concluded that "Semiarid" climate characteristics prevail in the south of Hilvan meteorological station and "Humid" climate characteristics in the north. The precipitation, which occurs 80% time equaled or exceeded, indicates "Arid" climate characteristics in the southern parts of the Harran district and "Semiarid" climate characteristics in the northern parts of the study area. Considering the area averaged precipitation values corresponding to the two exceeded or equalled the percentage of time ratios, i.e., 50% and 80%, it can be concluded that "Semiarid" climate characteristics are dominant in Sanliurfa province and its surrounding geography.

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Effect of Different Salt Concentrations on Germination of Forage Pea

KAYIN

TURAN

AYDEMİR>

2022

International Journal of Chemistry and Technology

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As a result of the rapid increase in the world population, the problem of balanced and adequate nutrition has emerged, and at this point, the importance of animal production has increased even more. In this respect, there is a need to increase the cultivation areas of forage crops, as well as the correct management and reclamation of pastures for adequate and balanced nutrition of our existing animals. For this reason, forage pea (Pisum sativum L.), which has high nutritional value and is preferred by animals, is an important forage crop to meet this need. Salinity significantly limits the growth of the plant and causes yield losses. This abiotic stress negatively affects forage peas. In order to reveal the differences in the germination of forage pea varieties at different salt concentrations and the differences in the physical properties of the roots and seedlings, the effect of different salt concentrations on nine different varieties of forage peas was carried out in Bilecik Şeyh Edebali University, Faculty of Agriculture and Natural Sciences, Agricultural Research and Biotechnology Center Laboratories in 2022. In the research, the effects of nine different field types (Ürünlü, Töre, Ateş, Guifredo, Taşkent, Özkaynak, Kurtbey, Uysal and Nany) at one control and eight different salt concentrations (25, 50, 75, 100, 125, 150, 175 and 200 mM) on germination and seedling growth were examined. The experiment was carried out according to Completely Randomized Factorial Designs with 3 replications. In the research; germination speed and power, root dry and fresh weight, root dry and fresh length, seedling dry and fresh weight, seedling dry and fresh length were examined. According to the results obtained, a significant decrease was observed in germination rate, germination strength, root-shoot length and wet-dry weights of forage pea varieties due to increasing salt concentration. It has been determined that Tore field pea variety has the highest germination rate and power. Tore field pea showed 100% germination speed and power up to 150 mM salt concentration. In addition, Töre variety is followed by Ateş variety in all parameters. The varieties with the lowest germination rate and vigour were Guifredo and Uysal. As a result, the growth of forage pea varieties slowed down due to increasing salt concentration.

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Tolerance of forage pea cultivars to salinity and drought stress during germination and seedling growth

Cited by 3 publications

References 13 publications

Using Machine Learning Algorithms to Investigate the Impact of Temperature Treatment and Salt Stress on Four Forage Peas (Pisum sativum var. arvense L.)

Using Machine Learning Algorithms to Investigate the Impact of Temperature Treatment and Salt Stress on Four Forage Peas (Pisum sativum var. arvense L.)

Developing and modeling precipitation duration curves and determining spatial and temporal distributions of precipitation over different percentages of time

Effect of Different Salt Concentrations on Germination of Forage Pea

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