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7РАЗВИТИЕ ЭЛЕМЕНТОВ СТРУКТУРЫ ПРОДУКТИВНОСТИ ЯРОВОГО ЯЧМЕНЯ В РАЗНЫХ ПОГОДНЫХ УСЛОВИЯХ ПЕРИОДА ВЕГЕТАЦИИ
Лисицын Е.М.
Федеральный Аграрный Научный Центр Северо-Востока
Киров, Российская Федерация
DEVELOPMENT OF BARLEY'S YIELD STRUCTURE COMPONENTS UNDER DIFFERENT WEATHER CONDITIONS OF GROWING SEASON
Lisitsyn E.
Federal Agricultural Research Center of the North-East,
Kirov, Russian Federation
АННОТАЦИЯ
На основе данных по развитию элементов структуры урожая пяти сортов ячменя (Hordeum vulgare L.), а также среднемесячных данные по температуре воздуха и количеству осадков за период вегетации ячменя (май - август 2002-2019 гг.) выявлено, что при линейной аппроксимации температурный режим и увлажненность периода вегетации за последние 18 лет практически не изменились. Коэффициенты детерминации не превышают 0,08. То есть, линейная аппроксимация не может характеризовать происходящие процессы изменения климата в условиях Кировской области. Повышение среднемесячной температуры воздуха, особенно в июне и августе, снижало развитие большинства элементов структуры продуктивности (68% наблюдений). Повышение количества осадков, напротив, способствовало развитию элементов продуктивности (в 62% наблюдений). Особенно благоприятно на растениях отражается повышение количества осадков, выпадающих в июне. Коэффициенты корреляций были значимыми на уровне р < 0.05 всего в двух парах признаков: число колосков в колосе - количество осадков в мае (r = 0.587); высота растений
- температура воздуха в июне (r = -0.600).
ABSTRACT
Based on data on the development of yield structure's elements of five cultivars of barley (Hordeum vulgare L.), as well as average monthly data on air temperature and precipitation for the period of barley vegetation (May
- August 2002-2019), it was revealed that with linear approximation, the temperature regime and humidification of the growing period over the past 18 years have not changed. The determination coefficients do not exceed 0.08. That is, linear approximation cannot characterize the ongoing processes of climate change in the conditions of the Kirov region. The increase in the average monthly air temperature, especially in June and August, reduced the development of most elements of the yield structure (68% of observations). The increase in precipitation, on the contrary, contributed to the development of productivity elements (in 62% of observations). Especially favorable on plants is the increase in the amount of precipitation in June. Correlation coefficients were significant at p < 0.05 in just two pairs of traits: the number of spikelets per ear - the amount of precipitation in May (r = 0.587); plant height - air temperature in June (r = -0.600).
Ключевые слова: сорт, осадки, температура, корреляции, регрессионный анализ, Кировская область.
Keywords: cultivar, precipitation, air temperature, correlations, regression analysis, Kirov region.
Introduction
To understand the processes leading to the formation of yield of agricultural crops, it is necessary to know the most significant factors of growing season, their relationship to the processes of growth and development of plants. To date, various approaches to conducting such studies have been used. Individual authors use averaged data for the whole country or for its specific regions [1, 2], summarizing yield data for individual plant species or even their groups (fodder, grain, or vegetable). At the same time, suggesting the use of statistical methods of correlation-regression analyses, many authors often indicate the possibility of distortion of their results in connection with existing agricultural trends [3]. Other authors [4] take into account the change in economic and valuable features of specific plant cultivars over a long period of observation. However, even here averaging occurs at the species level, without taking into account the possible influence of in-traspecific groupings. Therefore, the purpose of our study was a statistical analysis of the relationship of
weather conditions of growing season with the parameters of growth and development of spring barley plants, which is one of the main grain crops of the Kirov region [5, 6].
Materials and methods
The work used data on the development of yield structure's elements of barley (Hordeum vulgare L.) cultivars Novichok, Ecolog and Dina - double-row (nu-tans type); Tandem and Lel - multi-row barley (pal-lidum type) obtained at the Federal Agricultural Research Center of the North-East in 2002-2019, as well as monthly average data on air temperature and precipitation for the period of barley vegetation in the vicinity of Kirov (May - August 2002-2019). For August, data were analyzed only for the first decade of the month. Weather data are taken from official agro-meteorological bulletins issued by the Kirov Regional Center for Hydrometeorology and Environmental Monitoring.
Trends in weather-and-climate dynamics were calculated using the Microsoft Office Excel 2010 table calculator; multiple regression equations were calculated in StatSoft Statistica 10 software.
Results and discussion
Figures 1 and 2 show graphs of the dynamics of the average-month air temperature and precipitation for 2002-2019. Here are also the trend lines for each month, their equations and determination factors.
From these data, it can be concluded that with linear approximation, most often used to analyze such
phenomena, the temperature regime of vegetation, taking into account monthly averages over the past 18 years, has not changed practically. The determination coefficients do not exceed 0.08. In other words, linear approximation cannot characterize the ongoing processes of climate change in the conditions of the Kirov region.
Figure 1. Dynamics of the average monthly air temperature of the growing season (°C),
Kirov, 2°°2-2°19
Figure 2. Dynamics of the average monthly rainfall for the growing season (mm),
Kirov, 2°°2-2°19
The same is true for the nature of the change in the amount of precipitation during the growing season. In general, the data reviewed indicate the weak adequacy of linear models of changes in weather conditions, at least for the environs of Kirov: there is no reason to argue that there is a natural warming or cooling of the growing season, its aridization or, conversely, moistening.
In connection with the above, after that we considered the nature of the effect of the weather conditions of separate months of the growing season on the development of crop-forming parameters of barley plants using multiple regression methods.
First of all, let focus on the qualitative effects manifested in the sign before the coefficient reflecting the contribution of each of the eight factors studied (temperature and precipitation in four months) (Table).
Table.
Partial regression coefficients of equations of dependence of development characteristic on weather conditions of
vegetation (average for 5 barley cultivars, 2002-2019)
Parameter Air temperature Amount of precipitation
May June July August May June July August
Duration of growing season -0.456 -1.676 -2.138 -0.160 -0.021 0.167 0.014 -0.052
Plant height 1.015 -5.797 1.079 -2.576 0.550 0.299 0.021 -0.477
Tilling capacity 0.127 -0.164 -0.002 -0.151 -0.007 -0.001 -0.003 -0.029
Capacity for fruit-bearing shoots 0.063 -0.164 -0.017 -0.134 -0.003 0.003 -0.004 -0.032
Ear length -1.485 0.020 -0.129 1.637 0.060 0.066 0.008 -0.027
Number of spikelet 1.193 -0.655 0.281 -0.139 0.180 0.062 0.012 0.131
Number of grain per ear 0.954 -0.823 0.035 -0.145 0.197 0.072 -0.000 0.125
Grain mass per ear 0.048 -0.041 -0.009 -0.025 0.007 0.004 0.000 -0.005
Grain mass per plant 0.149 -0.113 0.062 -0.071 0.022 0.008 -0.005 0.016
1000-grain mass -0.070 -0.202 -0.329 -0.177 -0.013 0.040 0.011 -0.255
Productivity 0.118 -0.363 -0.262 -0.294 0.072 0.057 0.015 -0.063
As it can be seen, the same factor may have the opposite effect on different elements of the barley yield structure. However, the increase in air temperature in June and August negatively affects the development of all the studied elements of productivity, with the exception for the ear length. Conversely, precipitation in June contributes to their development. Further, an increase in the average monthly air temperature reduced the development of most elements of the yield structure (68% of observations), especially in June and August. The increase in precipitation, on the contrary, contributed to the development of productivity elements (in 62% of observations). Especially favorable for plants is the increase in the amount of precipitation falling in June.
Secondly, the effect of temperature is much higher than the number of precipitation. The part of the influence of the temperature factor on certain elements of the yield structure of barley plants exceeds the influence of precipitation sometimes by 2 degrees, more often by 3-5 times. One case can be distinguished from this general pattern: in June, precipitation has three times more influence on the formation of the ear length than air temperature. Of course, the mathematical relationship between some parameters should have a biological justification: for example, it did not make sense to look for the relationship of the trait "tilling capacity" with weather conditions after mid-June, when this trait had already formed and would not change further.
Multiple regression equations allow to take into account the influence of one or more factors on the resulting characteristic. However, particular coefficients of pair correlations are also of particular interest to researchers and breeders to assess the interdependence of the two specific parameters. Conducting such an analysis showed a sufficiently low level of correlations between development of elements of the plants structure of spring barley and weather conditions of the growing season. In particular, the coefficients of pair correlations in only two variants were statistically significant at p < 0.05: the number of spikelets per ear - the amount of precipitation in May (r = 0.587), the plant height -the air temperature in June (r = -0.600).
Naturally, such a low level of significant correlations could be explained by the fact that in this case a calculation was made for the average development parameters of five barley cultivars. In the case of individual cultivars, the number of significant correlations is much higher, but each cultivar has its own matrix of these relationships.
Conclusion
The conducted calculations show that the use of linear trends and averaged crop data would not adequately reflect the current situation when analyzing the possible impacts of climate change on cereal productivity.
References
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