In the coming years, Kazakhstan will continue to experience a reduction in the internal resources of river basins due to a decrease in the inflow of transboundary river waters from neighboring countries, the poor technical condition of water management infrastructure, a large share of irrigation water losses, climate change, and extremely low rates of adoption of water-saving technologies. The optimal use of irrigated lands is a key factor in increasing agricultural productivity, ensuring sustainable water use, and securing food safety. In the context of the Republic of Kazakhstan, the relevance of the topic is determined by the limited availability of water resources, high water losses under traditional irrigation methods, and the need to improve the economic efficiency of agricultural production. Studies emphasize the importance of introducing water-saving technologies (drip irrigation, sprinkler systems, subsurface irrigation, and regulated irrigation) and modernizing the water supply system to achieve efficient resource distribution and minimize operating costs. The purpose of this work is to optimize the cropping structure on irrigated lands through the use of water-saving technologies and irrigation equipment. This article examines the problem of obtaining the maximum net income from crop production on irrigated lands using a drip irrigation system, taking the peasant farm “Samgau” of Korday District, Zhambyl Region, as an example. This goal was achieved through economic-mathematical modeling of production processes on irrigated lands.
INTRODUCTION
Arid weather in southern Kazakhstan is one of the long-standing and widely discussed problems of irrigated agriculture. Water scarcity, reduced river runoff of transboundary rivers, and high losses during water conveyance through irrigation canals all lead to changes in the country’s development strategy in those agricultural sectors that require large amounts of water (Clark & Foster, 2025). In particular, it is proposed to reduce the acreage under water-intensive crops (rice, cotton), while agricultural enterprises are being strongly encouraged to introduce water-saving technologies.
In recent years, the country has seen a decline in water withdrawals from river basins for irrigated agriculture. If 12.1 billion cubic meters of river water were used for irrigation in 2020, by 2024, this indicator had decreased by 5.8% to 11.4 billion cubic meters. More than half of this volume came from the Aral-Syr Darya water management basin, whose resources are used by the most active southern regions, Turkestan and Kyzylorda oblasts. This is due both to the general reduction in river runoff in these basins and to their high anthropogenic pressure.
The Concept for the Development of the Water Resources Management System of the Republic of Kazakhstan for 2024–2030 emphasizes that in the near future, the country may face a significant water resource deficit amounting to 50% of demand. The rate of adoption of water-saving irrigation technologies in the Republic of Kazakhstan averages 30 thousand hectares per year, while the minimum required level is 150 thousand hectares. If the current pattern of water resource use by economic sectors and the population continues, by 2029, there is a risk of a slowdown in socio-economic development due to water scarcity. This may create a threat to the food and national security of Kazakhstan (Kyaw et al., 2024; Coleman et al., 2025).
The Concept provides for a number of measures to reduce the water resource deficit, including work on the comprehensive reconstruction of existing hydromeliorative systems with the wide introduction of water-saving technologies and irrigation equipment for crops.
The priority implementation of these measures is also relevant because agriculture in Kazakhstan currently consumes up to 70% of all water resources. In 2024, the total area of irrigated agricultural land amounted to 1.9 million hectares. Most of this area, 67%, received water through surface irrigation, while modern technologies such as drip irrigation and sprinkling were used on 26.3% of the area (or 397.3 thousand hectares).
Over the past 5–6 years, the country has seen renewed activity in the use of water-saving technologies and irrigation equipment for crops, although the area of irrigated land where sprinkling, drip irrigation systems, and discrete water supply are used accounts for less than 15% of irrigated land (Zar et al., 2024).
The key research areas in recent years have included objective programming of water resource allocation, economic optimization functions within mathematical modeling, the interface of biophysical crop yield models with economic criteria, as well as the application of advanced optimization and modeling algorithms (linear/nonlinear programming, stochastic modeling, Bayesian optimization) (Zakaev et al., 2024).
Literature review
Modern studies consider irrigated lands as integrated systems that include agrotechnical, hydraulic engineering, and economic components. The works of Abraliyev et al. (2024) and Usaeva (2024) show that a systemic approach to irrigation resource management makes it possible to increase crop yields by 15–25% while reducing water consumption by 10–20%. An important aspect is taking into account the specifics of soil and climatic conditions, crop type, and water consumption intensity (Nokusheva et al., 2023; Krasnova et al., 2025).
The introduction of water-saving technologies is regarded as the main instrument for improving irrigation efficiency. García-Mollá et al. (2025) demonstrate that drip irrigation and micro-irrigation provide a significant reduction in water losses and an increase in yields. Li et al. (2024) emphasize that the economic efficiency of technologies depends on their integration into the existing infrastructure and consideration of local hydrometeorological conditions. In Kazakhstan, the studies of Sadanova et al. (2025) confirm the positive effect of water-saving systems in the southern regions when growing cotton and vegetable crops.
The development of the water supply system includes the modernization of canals and pumping stations, the installation of measuring devices, and the automation of water distribution. Tulaganov et al. (2025) show that the systemic modernization of irrigation infrastructure makes it possible to reduce operational losses by 20–30% and improve control over water distribution among users. The use of remote monitoring and water balance management systems contributes to irrigation optimization and water savings (Shaimerdenova et al., 2026).
Thus, the publication by Zhang, Ren, and Zhao (2025) proposes an economic optimization model for the allocation of irrigation water for regions with resource scarcity. In this model, crop economics is linked with hydrological assessments, and the optimality criteria are based on the Karush-Kuhn-Tucker (KKT) conditions (Zhang et al., 2025). This makes it possible to distribute water in a balanced way so as to maximize yields while simultaneously equalizing the marginal benefit of water across the study area, which leads to a significant increase in gross harvest (Shi & Guo, 2024).
MATERIALS AND METHODS
Economic-mathematical modeling of irrigation optimization represents a methodological basis for water use planning, productivity improvement, reduction of operating costs, and enhancement of the sustainability of the agricultural sector under conditions of water scarcity (Abdullayev et al., 2025; Gumarova et al., 2025; Yskak et al., 2026). Under conditions of climatic uncertainty, population growth, and limited freshwater resources, the task is to develop models that make it possible to coordinate hydromechanical, biophysical, and economic parameters for the optimal allocation of water among crops, sectoral needs, and environmental priorities. The application of these models makes it possible not only to predict the results of introducing innovations, but also to develop optimal water use strategies within national agricultural development programs for different regions (Tashnichenko & Tregub, 2024).
The assessment of the economic efficiency of water-saving technologies and the modernization of the water supply system is part of managerial decision-making. The studies of Sajjad et al. (2025) and Perelli et al. (2024) show that investments in modern irrigation systems pay off within 3–5 years due to increased yields and reduced water and energy costs. In Kazakhstan, these indicators are confirmed by local studies by Abraliyev et al. (2024), Usaeva (2024), and Yessembay (2025), which demonstrate a positive correlation between the volume of investment in water-saving technologies and the growth of farm profits.
Main methods
Modern water-saving technologies considered in this study included drip irrigation, sprinkler irrigation, precision irrigation systems equipped with soil moisture sensors and automatic control, and laser land leveling to improve the uniformity of water distribution. Compared with traditional surface irrigation, these technologies are characterized by higher water-use efficiency and better adaptability to different agroclimatic conditions. In the conditions of southern Kazakhstan, their application is associated with more rational water allocation, improved use of irrigated land, and greater production efficiency (Usaeva, 2024).
The methodological basis of the study was economic-mathematical modeling aimed at optimizing the use of land and water resources in irrigated agriculture. The model was constructed to determine the crop structure that ensures maximum net profit under existing production constraints. In developing the model, resource limitations, irrigation norms, crop yields, production costs, selling prices, and the admissible proportions of crops in the farm structure were taken into account (Orynbekov, 2000; Onlassynov et al., 2024; Usaeva, 2024; Ismagulova et al., 2025; Tulaganov et al., 2025). The optimization problem belongs to the class of linear programming models and was used to assess the feasibility of introducing water-saving irrigation technologies in agricultural production. This approach makes it possible to substantiate management decisions on the allocation of irrigated land, the distribution of water resources, and the selection of the most economically efficient crop combinations (Orynbekov, 2000; Mukhamedzhanov et al., 2017; Logachev & Goncharov, 2024).
RESULTS AND DISCUSSION
The peasant farm “Samgau” is located in the irrigated farming zone in the south of Kazakhstan. In this region, there is a shortage of water resources for land irrigation. The irrigation systems were built almost 50 years ago, and their wear amounts to about 80% or more. At present, for the most part, irrigation of crops is carried out by flooding or along furrows. This leads to significant losses of irrigation water in the field as well. The irrigation norms recommended by science are, unfortunately, not observed. Therefore, a significant number of farms in the region, including PF “Samgau,” in order to save water, are shifting from traditional technologies and irrigation techniques for crops to water-saving technologies such as sprinkler irrigation and drip irrigation. In this situation, the task arises of determining irrigation priorities for crops, for example, using drip irrigation systems (Amanbayeva et al., 2022; Kudaibergenova et al., 2023). It is expected that the effect of using drip irrigation systems will be expressed in irrigation water savings, increased labor productivity, higher crop yields, and ultimately a reduction in the production cost of crops on irrigated lands (Belfiore et al., 2024; Figueroa-Valverde et al., 2024; Karatas, 2024; Lee & Ferreira, 2024; Negreiros & Ory, 2024; Wolderslund et al., 2024; Abdullah et al., 2025).
The following assumptions were adopted for the parameters of the proposed model of rational use of land and water resources, taking into account new innovative irrigation technologies and cultivation of crops in the peasant farm “Samgau”.
As a criterion of optimality for the use of land and water resources, the model implements the function:
|
|
(1) |
where:
F - is the net profit of the object (in this case, the peasant farm) from the sale of products of the j-th agricultural crop.
The net profit from the sale of products obtained from the cultivation of the j-th agricultural crop:
|
|
(2) |
(Mukhamedzhanov et al., 2017)
where:
- is the sown area of the j-th agricultural crop;
- is the sown area of the j-th agricultural crop;
- is the production cost of 1 centner of output of the j-th agricultural crop;
- is the selling price of 1 centner of output of the j-th agricultural crop.
The resources considered in the system are: S — the area allocated for crops, and W — irrigation water resources. As parameters of resource use, the model introduces 
- the irrigation norm of the j-th agricultural crop and 
– the structure of n crops.
With respect to the parameter 
- the crop structure, an additional condition is introduced 0,85
, or the combination of crops within one system, under the condition that the inertia of the latter cannot have a sharp jump and cannot be chosen arbitrarily.
First, the combination depends on agrotechnical conditions of crop rotation compliance; second, it may be dictated by market demand for finished products. Taking these two factors into account, we adopted a 15% tolerance for changes in this parameter compared with the previous year.
In addition to the above-listed parameters, the model introduces so-called “remark” parameters in order to account for the influence of new innovative technologies implemented in the system as a whole. Three directions of innovative technology are proposed: first, remark tv — new irrigation technology and equipment; second, remark tu — new cultivation agrotechnology; and third, tl — new technology in product sales logistics. Obviously, in one or another version of the experimental study, it is necessary to use data taking into account the introduction of one or another innovative technology (Kęska & Suchy, 2024; Noor et al., 2024; Jagsi et al., 2025; Schneider & Krüger, 2025; Wong et al., 2025, Yu et al., 2025). This is precisely the essence of the novelty of the proposed model of the rational management system and optimal use of land and water resources, taking into account innovative technologies (Csep et al., 2024; Ghiga et al., 2024; Kounatidis et al., 2024; Clark & Foster, 2025; Musa et al., 2025; Njoroge & Odhiambo, 2025; Petronis et al., 2025; Raza et al., 2025; Yu et al., 2025).
The mathematical model of the system for optimal use of land and water resources, taking into account new innovative technologies of irrigation and cultivation of crops, has the following structure.
|
|
(3) |
subject to the following constraints:
|
|
(4) |
|
|
(5) |
Where
In its structure, the model belongs to a mathematical linear programming problem, and in the general case, with a correct numerical formulation of the problem, it is solved with sufficient accuracy by the simplex algorithm. In particular, with a correct formulation of the problem, insolvability arises when selecting “absolutely zero” balances between the problem variables, which complicates the computational process and sometimes leads to cycling of the algorithm. Similar problems often arise in experimental calculations with a large volume of numerical information. In the model structure, “absolutely zero” balance conditions are replaced by softer conditions with the introduction, instead of “0,” of a sufficiently small number e > 0 (Orynbekov, 2000; Mukhamedzhanov et al., 2017).
To avoid computational problems, the linear programming model is presented in tabular form, convenient for using the simplex method algorithm (Table 1).
Table 1. Tabular model of the system for optimal use of water and land resources under the introduction of water-saving technologies and irrigation equipment.
|
|
I |
-X1 |
-X2 |
… |
-Xj |
|
-Xn |
-Y |
|
-1 |
S |
1 |
1 |
… |
1 |
… |
1 |
0 |
|
-2 |
W |
v1 |
v2 |
… |
vj |
… |
|
0 |
|
-3 |
0.1 |
1 |
1 |
… |
1 |
… |
1 |
-1 |
|
-4 |
- 0.1 |
- 1 |
0 |
|
0 |
|
0 |
a1 |
|
-5 |
- 0.1 |
0 |
-1 |
… |
0 |
… |
0 |
a2 |
|
… |
… |
… |
… |
… |
… |
… |
… |
… |
|
-i |
… |
… |
… |
.. |
-1 |
… |
… |
aj |
|
… |
… |
… |
… |
… |
… |
… |
… |
… |
|
-m |
-0.1 |
0 |
0 |
… |
0 |
0 |
-1 |
an |
|
F |
0 |
-m1 |
-m2 |
… |
-mj |
… |
-mn |
0 |
Table 2 presents the initial information necessary to solve the problem of optimizing the crop structure on irrigated lands. Here, the actual areas and crop structure averaged over the last 3 years are presented. The total area of irrigated land on the farm is 478.0 ha. The values of irrigation norms (in m³/ha) were established in accordance with the recommendations (Mambetnazarov et al., 2026). Yield indicators were averaged over the last 5 years. Selling prices are average market prices. According to the farm’s accounting data, the production cost of crops was calculated and is presented in Table 2.
Table 2. Technical and economic indicators of agricultural crop production on the irrigated lands of PF “Samgau” (averages for 2020–2024)
|
Name of crops |
Area under crops, ha |
aj – structure share |
Wj – irrigation norm, m3/ha |
uj – yield, c/ha |
pj – selling price, tenge/c |
cj – production cost, tenge/c |
|
Sugar beet (d.i*.) |
139,0 |
0,30 |
5500 |
1000,0 |
1900,0 |
1021,1 |
|
Grain corn (d.i.) |
61,0 |
0,13 |
4450 |
120,0 |
4500,0 |
1860,6 |
|
Spring barley (f.i.**) |
163,0 |
0,34 |
3650 |
24,0 |
3890,0 |
1842,8 |
|
Winter wheat (f.i.) |
28,5 |
0,06 |
3050 |
20,0 |
4250,0 |
2014,2 |
|
Vegetables (d.i.) |
45,0 |
0,9 |
5450 |
850,0 |
3600,0 |
2311,0 |
|
Soybeans (d.i.) |
15,0 |
0,03 |
3900 |
20,0 |
16500,0 |
2804,4 |
|
Perennial grasses (f.i.) |
26,5 |
0,05 |
8200 |
35,0 |
600,0 |
100,9 |
|
Total irrigated land |
478,0 |
1,0 |
|
|
|
|
|
*- drip irrigation; ** - flooding irrigation |
||||||
In accordance with the indicators presented in Table 2, a model was created for searching optimal options for the use of water and land resources in agricultural production on irrigated lands (Table 3).
Table 3. Model of rational development of crop production using drip irrigation technology in PF “Samgau” (for future implementation) (Mukhamedzhanov et al, 2017).
|
|
I |
-X1 |
-X2 |
-X3 |
-X4 |
-X5 |
-X6 |
-X7 |
-Y |
|
-1 |
478,0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
|
-2 |
3919,6 |
5,50 |
4,45 |
3,65 |
3,05 |
5,45 |
3,90 |
8,20 |
0 |
|
-3 |
0.1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
-1 |
|
-4 |
-0.1 |
-1 |
0 |
0 |
0 |
0 |
0 |
0 |
0.30 |
|
-5 |
-0.1 |
0 |
-1 |
0 |
0 |
0 |
0 |
0 |
0.13 |
|
-6 |
-0.1 |
0 |
0 |
-1 |
0 |
0 |
0 |
0 |
0.14 |
|
-7 |
-0.1 |
0 |
0 |
0 |
-1 |
0 |
0 |
0 |
0.06 |
|
-8 |
-0.1 |
0 |
0 |
0 |
0 |
-1 |
0 |
0 |
0.09 |
|
-9 |
-0.1 |
0 |
0 |
0 |
0 |
0 |
-1 |
0 |
0.03 |
|
-10 |
-01 |
0 |
0 |
0 |
0 |
0 |
0 |
-1 |
0.05 |
|
F |
0 |
-878,90 |
-316,728 |
-48,413 |
-44,716 |
-1095,6 |
-158,9 |
-17,46 |
0 |
Using the developed model and the baseline information indicators (Tables 2 and 3), as well as a computer program, an optimal solution to the problem was obtained (Table 4). The problem dimension is 10×8, the search goal is 1 — the maximum value of the objective functional (total net profit). The main required variables X are numbered by unsigned integers, and the additionally introduced variables Y in canonical form are numbered by signed negative integers (-).
Table 4. m = 10 n = 8 max min 1 optimal solution
|
|
|
-3 |
-5 |
-6 |
-7 |
-1 |
-9 |
-10 |
-4 |
|
-8 |
87.98 |
0.80 |
1.00 |
1.00 |
1.00 |
0.20 |
1.00 |
1.00 |
1.00 |
|
-2 |
1518.9 |
-0.42 |
-1.00 |
-1.80 |
-2.40 |
-5.03 |
-1.55 |
2.75 |
0.05 |
|
1 |
144.4 |
-0.30 |
0.00 |
0.00 |
0.00 |
0.30 |
0.00 |
0.00 |
-1.00 |
|
8 |
477.9 |
-1.00 |
0.00 |
0.00 |
0.00 |
1.00 |
0.00 |
0.00 |
0.00 |
|
2 |
63.23 |
-0.13 |
-1.00 |
0.00 |
0.00 |
0.13 |
0.00 |
0.00 |
0.00 |
|
3 |
68.01 |
-0.14 |
0.00 |
-1.00 |
0.00 |
0.14 |
0.00 |
0.00 |
0.00 |
|
4 |
29.77 |
-0.06 |
0.00 |
0.00 |
-1.00 |
0.06 |
0.00 |
0.00 |
0.00 |
|
5 |
132.0 |
-0.71 |
1.00 |
1.00 |
1.00 |
0.29 |
1.00 |
1.00 |
1.00 |
|
6 |
15.44 |
-0.03 |
0.00 |
0.00 |
0.00 |
0.03 |
-1.00 |
0.00 |
0.00 |
|
7 |
25.00 |
-0.05 |
0.00 |
0.00 |
0.00 |
0.05 |
0.00 |
-1.00 |
0.00 |
|
|
299254.8 |
-457.9 |
778.9 |
1047.2 |
1050.9 |
637.7 |
935.7 |
1078.1 |
216.7 |
|
number of iterations 8 |
|||||||||
The optimal variant provides for a slight expansion of the area under sugar beet by 5.47 ha, grain corn by 2.23 ha, a reduction of almost 100.0 ha under barley, and an expansion of the area under vegetable crops by 87.96 ha, while changes in the sowing areas of other crops are insignificant. The total developed area of irrigated land is 477.9 ha. The optimal variant provides for savings of irrigation water in the amount of 1,518.96 thousand m³. At the same time, the maximum total net profit was obtained in the amount of more than 299.0 million tenge.
The result of solving the problem for the selected example provides for obtaining a net profit of more than 299.0 million tenge. This is achieved mainly due to an increase in the sowing area under vegetable crops and a slight increase in the sowing areas under sugar beet and grain corn.
An optimal variant for the placement of crops on the irrigated lands of the peasant farm “Samgau” under the introduction of drip irrigation systems was obtained.
When writing the application software, the Delphi programming language was used — an imperative structured object-oriented programming language with strict static typing of variables. The developed approach may be used by agricultural producers to solve similar tasks for planning crop placement on irrigated lands and obtaining maximum profit.
Key empirical data confirm that such technologies are capable of reducing water consumption by 30–50% compared with traditional methods, which is especially important for regions with scarce water resources. The efficiency of water distribution when using sprinkler systems was also studied.
The efficiency of water-saving technologies is closely related to water supply infrastructure, the water distribution management system, and government policy. The water supply system includes irrigation canals, pumping stations, measuring devices, and digital management platforms (Krasnikova et al., 2024). Investments in the modernization of water infrastructure, including canal reconstruction and automation of distribution, are a necessary condition for the implementation of water-saving technologies (Krasnikov & Smirnova, 2024).
Numerous government programs in Kazakhstan are aimed at increasing the area under modern irrigation systems, expanding the regulation of water use, and increasing subsidies for farmers. For example, the practice of government subsidizing the installation of water-saving systems and benefits for farmers shows that the use of these technologies is stimulated at the legislative level, which increases their dissemination and economic attractiveness in the agricultural sector (Battalova & Enikeev, 2024).
The economic aspects of implementing water-saving technologies include estimates of installation and operating costs, reduction of operating expenses, yield increases, and growth in farm incomes (Logachev & Smirnova, 2024). Based on empirical data, it is noted that:
• The introduction of drip and sprinkler irrigation makes it possible to significantly reduce operating costs (energy, water, and labor).
• Reduction of water losses increases water use efficiency and lowers production costs.
• The use of innovative systems often leads to improved physiological indicators of plants and increased yields, thereby increasing gross output per irrigated area.
Studies confirm that although initial investments in the modernization of irrigation systems and the application of water-saving technologies may be high, economic benefits are achieved through increased yields of crops, higher profitability, and expansion of productive areas, which makes such technologies economically justified in the long term (Jin et al., 2024; Osluf et al., 2024; Rypel et al., 2024; Joungtrakul & Smith, 2025; Kebe et al., 2025).
The main problems in implementing water-saving technologies and irrigation equipment include:
• Limited financial resources for modernization.
• High wear of irrigation infrastructure.
• Low level of personnel training in new technologies.
• Lack of a unified water accounting information system.
Promising directions include public-private partnership, government subsidization, digitalization of water supply systems, and integration of modern water-saving technologies (Grankina & Vasilyev, 2024).
CONCLUSION
The article presents the results of solving the problem of optimizing production costs for cultivating crops on irrigated lands when using drip irrigation systems. The problem was solved using economic-mathematical methods.
Modern water-saving technologies and integrated water supply systems are the main elements of the strategy for the optimal use of irrigated lands. Scientific publications of recent years demonstrate that innovative irrigation methods ensure a substantial reduction in water consumption, increased productivity, and improved economic efficiency of production. However, successful implementation requires comprehensive approaches, including infrastructure modernization, government support, farmer training, and access to financing.
Studies of the use of water-saving irrigation technologies by agricultural enterprises for 2020–2024 emphasize the need for interdisciplinary integration of technical, economic, and institutional aspects in the development and implementation of strategies for the optimal use of water resources. It was shown that modern technologies adapted to the local conditions of Kazakhstan have the potential to significantly improve the sustainability of the agricultural sector and strengthen water management under conditions of climate change and growing demand for food.
Thus, the optimal use of irrigated lands through the application of water-saving technologies contributes to the development of water supply systems in agricultural production.
ACKNOWLEDGMENTS: None
CONFLICT OF INTEREST: None
FINANCIAL SUPPORT: This research is being carried out within the framework of budget program 267 “Increasing the Accessibility of Knowledge and Scientific Research” for 2024–2026, subprogram 101 “Program-targeted financing of scientific research and activities,” under specification 154 “Payment for research services,” activity: “Development of organizational and economic measures to improve the efficiency of the use of the resource potential of agricultural production” No. BR 22886885.
ETHICS STATEMENT: None
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