Algorithmization of bioproductivity management of winter grain crops adjusting the Selyaninov hydrothermal coefficient in the soiland climatic conditions of Southern Russia

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Introduction

Management of production processes in irrigated agriculture based on consideration of agroclimatic conditions for cultivating winter grain crops is an important task, the solution of which is relevant for assessing the effectiveness of ongoing agrotechnical and reclamation measures. This is particularly true for the arid soil and climatic conditions of southern Russia, where it is necessary to consider a combination of agro-climatic, biological, and technological factors of crop cultivation [1–3].

In extremely arid conditions, the structure of crop rotation should be selected considering not only the required production volumes in the area under consideration, but also crop vulnerability considering adverse weather events, drought probability, and their characteristics [4–7].

Increasing soil moisture content through water reclamation can be considered a traditional method of combating droughts and hot dry winds [8–11]. However, the main obstacle to the practical application of irrigation reclamation is the shortage of water resources. Another limitation of this approach, which significantly restricts its practical application even when water sources are available, is the significant investment of material and financial resources.

According to numerous studies, these problems will worsen over time due to the observed aridization of soil and climatic conditions and global warming. This justifies the relevance of developments that improve grain crop cultivation technologies under challenging natural and economic conditions, including hydrothermal ones.

The goal of the research was to eliminate the biotechnological contradiction between high yields of low quality (forage use) and low return on production costs associated with producing high-quality grain.

Materials and methods

In addition to analyzing scientific and technical literature in the research area, patent search was conducted using selected rubrics of the International Patent Classification (IPC) A01G 7/00; A01G 22/00; G01W 1/02, etc. Patent research revealed a number of known methods for managing production processes, including those based on predicting grain yield and quality, as well as methods for reducing risks in agricultural production associated with natural phenomena hazardous to agriculture [5].

Methods of artificial precipitation enhancement from clouds, active cloud modification, etc. (including IPC G01W 1/02) were also analyzed. To develop a technology for crop production considering the influence of hydrothermal cultivation conditions, an analysis of the soil and climatic conditions of the Volgograd Region was carried out, selected as a typical representative of arid farming conditions in Southern Russia.

A distinctive feature of the Volgograd Region, a typical Lower Volga region in terms of crop yield variability, is its extreme continental climate, including even early summer droughts. Years (e. g., 2010) were noted in which 4–18 mm of precipitation was observed in May-J une against a norm of over 30 mm. The location and natural and climatic conditions of the Volgograd Region are characterized as a risky farming zone.

Consideration of hydrothermal conditions for crop cultivation was carried out using the Selyaninov hydrothermal coefficient (HTC), measured by the ratio of precipitation to the sum of temperatures.

The calculation of HTC, mm/°C, was performed using the dependence:

\( G_{sel} =10 \cdot \frac{\Sigma P}{\Sigma t}, \)                                        (1)

where G_sel is the numerical value of HTC, mm/°C; ΣР is the total precipitation over the period during which the air temperature was above +10 °C, mm; Σt is the integral value of the sum of positive temperatures calculated for the same period, °C.

It should be noted that HTC is a complex indicator of heat and moisture availability of cultivation conditions, characterizing the evolving climatic conditions of the agricultural year in terms of heat and moisture resource availability. Modeling the interannual variability of HTC values during vegetation periods is a separate problem solved by various calculation and empirical methods, for example [12].

When developing the described technology, methods of mathematical and statistical modeling of crop yields depending on agrotechnological and natural- climatic factors described in the scientific and technical literature [6–11] were used. Regional recommendations for selecting agrotechnical parameters for cultivating cereal crops in arid conditions of the Volgograd region are available [13–16].

When substantiating rational seeding rates, the proportion of plants that do not survive until harvest period should be considered. In particular, when cultivating wheat on chernozem soils, the planting density should not be lower than 500–600 plants/m². When sowing quality seeds on chestnut soils, the planting density can be reduced to 300–450 plants/m². In the steppe and dry-steppe zones of the Volgograd region of the Russian Federation, with calendar sowing dates from August 25 to September 5, it is recommended to set the seeding rate on chestnut and dark chestnut soils at 3.5–4.0 million seeds per ha. On light chestnut soil in the semi-desert zone, it is optimal to sow winter crops in the period from September 1 to 10 with a seeding rate of 2.5…3.0 million seeds per ha at soil and air temperature during the sowing period of at least +18°…+12 °C.

Results and discussion

During a patent search conducted to develop a method for rapidly assessing potential productivity of agricultural plants, the method [6] was selected as the baseline. This method involved selecting seeds, soaking them in an aqueous solution of potassium salt, and instrumentally measuring the rate of increase in hydrogen ion concentration. The authors [6] found that the estimated potential productivity of plants is linearly dependent on this characteristic.

The method described simplifies the diagnostic technology and contributes to increasing the reliability of productivity assessments. However, a drawback is the limited range of predictions, as it is difficult to scale to other regions with different conditions.

Patent research reveals a method for assessing productivity of winter grain crops, such as winter wheat and barley, which involves optimizing the timing and rates of seeding. It is proposed to ensure that the sum of temperatures accumulated during the growing season is equal to 600 °C [1].

[2] describes a method for assessing the yield of winter grain crops grown in arid climates, including optimization of agronomic parameters at sowing temperatures within +12…+18 °C. A drawback of this method, which necessitates its improvement, is the lack of recommendations for adjusting agronomic parameters in accordance with the evolving climatic conditions of heat and moisture availability.

On the zoning map of agroclimatic indicators of the Volgograd region territory, including the sum of temperatures during the growing season exceeding 10 °C and the hydrothermal isolines of the Gsel coefficient (Fig. 1), dashed lines show the boundaries of administrative districts, and numerical and letter designations indicate their numbers. The contours of typical isolines of total temperatures are shown at intervals of 200 °C, and the values of hydrothermal coefficients are shown at intervals of 0.1.

In connection with the foregoing, the task was set to develop a technology for cultivating winter grain crops and predicting grain productivity and quality based on phenological observations.

To solve the problem, a “Method for Managing Agricultural Plant Production Processes in Extreme Continental Climate Conditions” [4] was developed, Patent RUS no. 2228607.

The developed modified technology for assessing potential productivity is based on optimizing agronomic timing and seeding rates and has been tested in the Volgograd Region.

The algorithmic basis of the proposed method is the adjustment of the seeding rate using the calculated numerical value of the HTC Gsel, determined for the grain crop growing region.

The algorithm for accounting for the influence of the HTC provides for the following adjustments to agronomic parameters depending on its value:

• At G_sel < 0.5, the seeding rate is reduced by 10–15% of the calculated values, and the row spacing is increased from 15 to 22.5 cm;
• At G_sel within 0.5…0.9, the seeding rate is maintained at recommended values;
• At G_sel > 0.9, the seeding rate is increased by 20–25% with a reduction in row spacing to 7.5 cm.

Fig. 1. Agroclimatic zoning of the Volgograd Region
Source: based on materials from the Agroclimatic Handbook of the Volgograd Region ^[1].

Thus, bioproductivity management during grain cultivation is achieved by accounting for the HTC value, which characterizes their actual heat and moisture availability. The HTC value is determined by calculation or graphic analysis. Implementation of the described approach allows simultaneous numerical estimation of winter grain crop yields using the formula

\( Y_{03} = a G_{sel} S +b, \)                                                           (2)

where Y₀₃ — the estimated yield, t/ha; a and b — parameters determined based on longterm empirical data, for example, using the least squares method; S — the cumulative sum of positive temperatures from sowing to the end of the growing season, °C; G_sel — the hydrothermal coefficient adopted for the agricultural production region, mm/°C.

The results of field studies examining the effect of seeding rate on yield and grain quality of winter wheat cv. Donshchina are presented in Table 1. The row spacing was 22.5 cm with a seeding depth of 5–6 cm. Field experiments were conducted from September 6 to 8 on chestnut soils in the dry steppe zone of the Volgograd Region.

The G_sel value for the experimental farm was assumed to be 0.9.

Based on long-term observations, the values in dependence (2) were:

a = 0.008; b = 0.390.

The temperature sum constant was assumed to be S = 600 °C.

Numerical calculation yielded a point estimate of productivity forecast for next year using dependence (2), which was

           Yзк = 0.008 · 600 · 0.9 + 0.390 =  4,71 t/hа.                                                      (3)

The point estimate of the productivity forecast for winter wheat cv. Don 95 for S = 585 °C, calculated using dependence (2), was

           Yзк = 0.008 · 585 · 1.3 + 0.390 =  6,74 t/hа.                                                      (4)

Table 1
Effect of seeding rate on productivity and grain quality of winter wheat cv. ‘Donshchina’

Seeding rate, 106 seeds/ha	Yield,  t/ha	Plant loss  in autumn-w inter period, %	Number of stems per m² at harvesting	Protein content, %	Gluten, %
1.5	2.83	11.4	275	10.6	23.1
2.0	3.12	10.7	316	12.7	28.5
2.5	4.27	9.2	362	14.3	32.4
3.0	4.53	7.3	389	15.1	34.7
3.5	4.63	6.9	426	16.2	37.3
3.6	4.71	5.2	458	15.8	35.1
3.7	4.72	5.8	482	15.1	33.1
3.8	4.58	6.3	492	14.2	30.2
3.9	3.87	6.6	496	13.6	28.1
4.0	3.62	6.7	472	10.6	27.6

Source: compiled by D.A. Rogachev, A.F. Rogachev based on data from [4].

The influence of seeding rate on the yield of winter wheat cv. Donshchina, approximated by a quadratic polynomial, is shown in Fig. 2.

The regression dependence of productivity of winter wheat cv. Donshchina on seeding rate is approximated by formula with acceptable accuracy (determination coefficient R² = 0.7943).

Y = –0.7708x² + 4.8519x – 3.0214,                                (5)

where x — the seeding rate, million seeds per ha.

Fig. 2. Effect of seeding rate on productivity of winter wheat cv. ‘Donshchina’
Source: compiled by D.A. Rogachev, A.F. Rogachev.

Experiments have shown that row spacing in winter wheat cultivation can also determine grain quality [4], as illustrated by the data presented (Table 2).

Table 2
Effect of row spacing on yield Y, t/ha, and grain quality  (protein content P and gluten content G, %)

Cultivar					Row spacing, cm
		7.5			22.5		30.0				15.0
	Y	P	G	Y	P	G	Y	P	G	Y	P	G
1. Don 93	4.83	17.1	34.7	4.37	16.8	33.2	4.15	15.7	30.2	3.37	12.3	29.2
2. Don 95	4.13	16.2	30.2	3.99	16.1	29.8	3.78	15.6	28.7	3.15	14.2	26.3
3. Volgogradskaya 84	6.31	16.4	35.3	5.87	15.8	33.3	4.13	15.1	31.8	3.27	14.8	29.7
4. Donshchina	3.36	16.2	37.3	3.12	15.8	36.2	2.87	12.6	31.2	2.13	10.6	23.1
5. Krasnodarskaya 39	3.17	14.2	36.3	2.85	13.8	33.0	2.67	13.1	32.6	2.12	12.4	29.8
6. Mironovskaya yubileinaya	4.15	18.6	38.6	3.86	17.9	37.4	3.41	16.3	34.3	29.7	12.7	31.3
7. Saratovskaya 90	3.09	14.4	30.3	5.72	15.8	32.3	4.13	13.6	28.4	3.62	12.4	24.9

Source: compiled by D.A. Rogachev, A.F. Rogachev based on data from [4].

When studying the influence of row spacing on yield, row spacing values less than 7.5 cm were not considered due to the impossibility of technological implementation (Fig. 3).

Fig. 3. Effect of row spacing on yield of winter wheat cv. ‘Donshchina’
Source: compiled by D.A. Rogachev, A.F. Rogachev.

The regression dependence of productivity of winter wheat cv. Donshchina on row spacing is approximated by formula with a determination coefficient of R² = 0.9865.

          Y = –0.0022x² + 0.0308x + 3.23,                                                                       (6)

where x — the row spacing, cm.

The productivity values of the analyzed winter wheat cultivars are consistent with calculations performed using formula (2), with acceptable agronomic accuracy, while the discrepancy between calculated and experimental data is within acceptable limits (LSD < 0.05).

Conclusion

The developed technology for managing the biological productivity of winter crops considering zonal hydrothermal coefficient values characteristic of the arid conditions of southern Russia, enables the management of processes for achieving biological productivity of winter grain crops in arid soil and climatic conditions while simultaneously assessing their potential yield.

The sufficiently high convergence of actual winter crop yield values with the predicted assessment of their potential productivity (LSD < 0.05) enables planning grain agricultural production results ensuring programmable efficiency for the following economic year.

 

_{¹ Rusiv ZM, Tsarkov LI. (eds.) Agroklimaticheskii spravochnik po Volgogradskoi oblasti [Agroclimatic handbook for the Volgograd region]. 2nd ed. Leningrad; 1967.}

About the authors

Dmitry A. Rogachev

Russian Research Center for Hydraulic Engineering and Land Reclamation named after A.N. Kostyakov

Email: Rogachev.soft@gmail.com
ORCID iD: 0009-0003-4014-4770
SPIN-code: 1948-7354

Candidate of Engineering Sciences, Leading Researcher

44 Bolshaya Akademicheskaya st., Moscow, 127550, Russian Federation

Aleksey F. Rogachev

Volgograd State Agrarian University

Author for correspondence.
Email: rafr@mail.ru
ORCID iD: 0000-0002-3077-6622
SPIN-code: 8413-5020

Doctor of Engineering Sciences, Professor, Department of Mathematical Modeling and Computer Science

26 Universitetsky ave., Volgograd, 400002, Russian Federation

References

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