Dynamic Assessment of the Use of Natural Resources in an Agricultural Production Chain

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1. Introduction

During the 20th century, there was a notable increase in agricultural production, mainly linked to population growth. Between 1961 and 2000, the world’s population grew by 98%, increasing food production by approximately 146%. While crop yields multiplied, the area of arable land in use increased by only 8%, which was made possible by the use of advanced technologies, increased agricultural inputs, and chemical fertilizers [1].

To meet the growing demand for food caused by the increase in population, it is necessary to accelerate the growth and transformation of agriculture by increasing productive performance [1]. The economic development of the agricultural sector is tightly linked to innovations and to the use of capital-intensive inputs, both requiring capital and investments, which are often not available to farmers [2, 3].

Climate change is a serious threat and challenge for the human race. The increasing temperature, environmental repercussions, land deterioration, rainfall fluctuation, precipitations, ecological deficit, and greenhouse gas (GHG) emissions are challenges to the survival of economic and noneconomic sectors. Environmental issues and rising pollution have become a threat to agriculture, industrialization, and food security [4].

Climate change is one of the challenging environmental concerns for development because of its impact on water security, especially in arid and semiarid regions. Increased evapotranspiration from agriculture is anticipated in the coming years due to the expected changes in temperature, precipitation intensity, annual amount, temporal distribution, atmospheric water vapor, and soil water content [5].

The Sustainable Development Goals (SDGs) are endangered due to the increasing scarcity of water and its impacts on food production (SDGs 2014). In particular, the SDGs related to hunger, poverty, and environmental sustainability are threatened by population increase, urban sprawl, and climate change. As a consequence, the management of water resources has become an increasingly challenging job from a technological, economic, social, and political perspective [6].

Based on statements of the Food and Agriculture Organization (FAO) of the United Nations, sustainable development in agriculture is always accompanied by the preservation of natural resources, such as soil and water. For this reason, it is appropriate to study the availability of these products since their moderate use greatly influences the increase in agricultural production.

In light of the above, the nations are putting pressure on water supplies. According to projections, the world will experience a 40% deficit between expected demand and the available supply of water by 2030 if current practices continue [7].

On the other hand, studies have revealed that approximately 70% (up to 95% in developing countries) of groundwater and surface water extraction in the world is due to agriculture [8]. In Colombia, the water required by the agricultural sector corresponds to 56% of the total water demand; however, there are not enough irrigation districts to distribute the appropriate amount of water to crops, due to the high costs of their implementation, lack of control, and oversight by governmental bodies, leading to indiscriminate and inefficient resource consumption [9].

Healthy soils are required to meet food needs since approximately 95% of food comes from soil, demonstrating the importance of this resource’s availability and quality. In Colombia, 40% of the continental surface shows mild, moderate, or very severe erosion. Additionally, soil is underutilized in agriculture since only 5 million hectares are being used for this purpose, out of a total of 22 million hectares with agricultural potential.

Due to the impact of water and soil consumption on agricultural production chains, it is important to develop a tool that can simulate the behaviour of these elements, taking into account crop performance, the fluctuating availability of resources, and the complexity of agricultural systems over time [10].

System dynamics (SD) is the appropriate methodology for this purpose, as it allows for the study of the effects of natural resources on crop yields and serves as a support for policy approaches that help conserve resources and thereby lead to increased yields of agricultural production.

This paper is organized as follows: a review of the relevant and current literature on the topic is presented, then the materials and methods present the methodology and detail its application in an area of Colombia, the results and their discussion are presented, and the final is the conclusions.

2. Literature Review

SD is a discipline that allows long-term study of the behaviour of complex systems from the feedback relationships between variables, by studying both the structure and behaviour of systems. It is particularly recommended for complex systems with nonlinear relationships, and in cases that exhibit a time delay between the actions taken and the expected consequences of those actions [11].

When reviewing the literature on applications of SD in agriculture, most studies focus on the effects of this activity on two major resources: water and soil. This is explained and supported by the importance that these resources have not only for agricultural activities in particular but for the survival of humanity in general.

These two resources are very important and have not only been addressed from the perspective of systems dynamics, but it is important to mention studies that focus on the use of these resources in agriculture. Among these works, they can mention [12] the exploration of the impacts of agricultural productivity and alternative energy sources on natural resources, considering the roles of imports of goods and services and economic growth in China from 1980 to 2018, in the same vein. Soulé et al. [13] present a systematic review of environmental sustainability assessment in agricultural systems; Mwambo et al. [14] modelled five scenarios of land use and resource management practices for maize production; de Almeida Telles et al. [15] developed an optimization model that allows for both optimal production planning and its updating, without land exploitation; Alhashim, Deepa, and Anandhi [16] consider the environmental impact of agricultural activities; and El-Rawy et al. [17] investigate the expected adverse impacts of climate change on water irrigation management in Saudi Arabia.

Concerning specific works associated with water resources and their impact on agricultural activities, mention should be made of [18] who developed a SD model, for simulating transient soil water flow, solute transport, and root water uptake in crops underwater and salinity stress in a multilayered unsaturated soil layer. Turner et al. [19] identified numerous SD cases applied to water, soil, food systems, and smallholder issues. On the other hand, some SD models explore the effects of agriculture on soil resources, such as [20] those who explore different scenarios of land use for agricultural activities and housing, so that policies can be defined against this use. Turner and Kodali [21] present an integrated soil-water-nutrient-plant interaction model to replicate soil moisture evolution for a set of unique soils and climates, examining model performance given common irrigation and crop management considerations, and evaluating via sensitivity analysis model robustness and quantifying influential management parameter effect on core biophysical feedback at the soil level.

The study of agriculture from the point of view of SD includes works that approach the study from different perspectives. Some works concentrate on identifying leverage points for effective climate change adaptation measures [22], while others involve not only agricultural activities but also resources such as food and energy [23]; on the other hand, the possible result of applying green agriculture has been evaluated [24] or the relationship between the urbanization and agriculture sector land on environmental pollution [4].

Finally, it should be mentioned that some of the most recent applications found in the literature review using SD for analysis and evaluation of scenarios in agricultural production chains are rice cultivation and its commercialization [25], the cocoa value chain [26–28], rubber cultivation in small-scale farmer units [29], cereal crop production [30], vegetable supply chain [31], forage crops and livestock production in small-scale farmer families [32], and assessment of small and low-scale mango and macadamia nut producers’ production systems [33].

3. Materials and Methods

3.1. The Supply Chain of Bulb Onion Crop in Boyacá

Bulb onion (Allium cepa L.) is classified as a vegetable and has nutritional, socioeconomic, and medicinal importance worldwide [34].

It is one of the most produced agricultural products on the planet, achieving more than 5 million hectares of area harvested worldwide and production above 104.5 million tons in 2020. In Colombia in 2019, the total area harvested was 15,668 hectares, which generated production of 399,511 tons, placing this crop in fourth place within vegetables, with a 13% share in production. The production process is presented in Table 1.

Table 1

Production process.

Stage
Soil preparation	Seedling production	Transplantation and planting	Harvest	Postharvest
– Soil analysis to determine physicochemical requirements.– Type of tillage.– Construction of irrigation and drainage channels, construction of crop rows.– Define amendments and correctives for soil, among others.	– In seedbeds (ventilated place, lighting, access to the water resource in quantity and quality of water, with a fertile substrate, frank texture– Clayey frank, drained and disinfected.– Pest control, weeds, irrigation systems, and fertilization plan.	– Direct sowing or transplantation.– Irrigation to prepare the ground.– Placing seedlings manually or mechanically in the field.– Weed, pest, and disease controls	– The plant is started manually or by hoe, shaken, and left to dry in the sun for 2–3 days.	– Cutting of stems, dry tunics, and root.– Quality control and/or curing to promote the preservation of the product.
Time (day)	40–50	120

The department of Boyacá is located in the center of the country, in the eastern Andes mountain range. It is located between 04°39${}^{\prime }$10${}^{″}$ and 07°03${}^{\prime }$17${}^{″}$ north latitude and 71°57${}^{\prime }$49${}^{″}$ and 74°41${}^{\prime }$35${}^{″}$ west longitude. It has an area of 23,189 km², which represents 2.03% of the national territory.

In the territory of Boyacá, there is a diversity of geographical features that form the physiognomic regions of the Magdalena River valley, the Cordillera Oriental, the Altiplano Cundiboyacense, and the foothills of the eastern plains. Thanks to this, all the thermal floors with temperatures from 35°C in Puerto Boyaca are present in the department. Temperatures are below 0°C in the Sierra Nevada de Güican and El Cocuy, which have heights of up to 5490 m, and in the Paramo of Pisba with heights of up to 4000 m.

The department of Boyacá is the main producer, with 207,371 tons registered in 2019, which is equivalent to 51.9% of the national production. According to the Departmental Agricultural Evaluation, in 2019 in Boyacá, bulb onion production was recorded in 53 municipalities, of which Toca, Siachoque, and Samacá stood out, accounting for 20.9%, 11.6%, and 10.8% of departmental production, respectively (Figure 1).

[figure(s) omitted; refer to PDF]

Ninety-nine percent of the harvested volume is consumed nationally, mainly in Bogotá, Cúcuta, Cali, and Medellín.

3.2. Management and Use of Natural Resources in Agriculture

The efficient and sustainable use of natural resources contributes to the improvement of yield, productivity, and, therefore, the competitiveness of agricultural production chains [12, 35]. Factors such as climate instability promote the excessive use of water, a resource that can sometimes be difficult to access. On the other hand, the low quality of soils and their geographical characteristics are directly reflected in production, causing losses in the economy of the producer [12, 13].

3.2.1. Water Resources

In agricultural production chains, water resources are used mainly for irrigation activities, and the objective of this process is to provide crops with additional rainwater necessary for growth and proper development [36]. The amount of water to be applied depends on the age of the crop, soil characteristics, and weather conditions.

The inefficient use of water through irrigation systems, in addition to causing scarcity of the resource and limiting the proper growth of the crop, generates high soil moisture, which favours the development of diseases caused by fungi and bacteria, affecting the safety of the product and the health of people and animals.

3.2.2. Soil Resources

The activities carried out by the fruit and vegetable sector have led to erosion, leaching, reduction in fertility, loss and imbalance of nutrients, displacement of species, loss of biological diversity, and accelerated transformation of natural habitats [37].

Sixty percent of the area in the Alto Chicamocha irrigation district has soils with sulfation and acidity, mainly due to inadequate agronomic management [38], industrial pollution, and natural phenomena. This problem reduces the physicochemical characteristics of the soil, affecting the yield of production and the development of the region, and it becomes essential to make improvements in the soils through the use of conditioners, mostly of a chemical nature.

Due to agricultural production, there is also a negative effect on the soil due to the indiscriminate use of fertilizers and pesticides, which contributes to the contamination of this resource and affects not only crop yields but also human health and the safety of agricultural products.

3.2.3. Use of Natural Resources

To develop the cultivation of this bulb onion, specific conditions of the soil resources and climatic conditions are required and thus achieve adequate performance according to the production site, as shown in Table 2.

Table 2

Use of natural resources.

Soil resource	Water resources	Climate
Light loam or clay loam textureGood moisture retentionpH (6–6.8)	Adequate rainfall range during the crop cycle (400–1500 mm of water)Watering every 2–3 days a week according to stage	Altitudinal range from 0 to 2800 m above sea levelTemperature: 12°C–23°CCrop sensitive to drought conditions and high temperatures

Taking into account the different stages in Table 3, the consumption of water resources is observed according to the stage of development of the crop.

Table 3

Use of natural resources per stage.

Requirement	Unit	Transplantation	Vegetative development	Bulb formation	Maturation
Water resources	mm		46.7–104.9	103.9–120.47	51.72–61.65
Time	Days	40–50		120

3.3. SD

SD is a modelling and simulation technique based on system thinking, introduced in the 1960s by Professor Jay Forrester of the Massachusetts Institute of Technology. This methodology is based on the assumption that the structural relationships between the elements of a system may be more important in determining the behaviour of the aggregate system than the individual components themselves [11].

The use of SD in the study of agricultural and environmental problems has increased in recent decades since the complexity and size of the system; the scarcity of resources and the long periods required to see changes are some of the factors that make the use of dynamic simulation models in this type of problem [19].

The analysis of systems using this technique is mainly based on the study of the interactions between the elements of this system, which can be used to demonstrate how a change in a specific element will influence the general behaviour of the system [39]. Identifying these relationships allows a better understanding of the system, which can determine the actions that will help improve it.

For the development of the SD model, the methodology followed Sterman’s proposed steps [33]. Firstly, the construction of the influence diagram or dynamic hypothesis involved representing the main variables and their feedback relationships, with a primary focus on water resources and soil considering crop development. Once the influence diagram was established, providing an overview of the variables and their relationships, the Forrester diagram was developed. This formalized the model, utilizing representations of level and flow variables along with the mathematical formulations connecting them. The model consisted of three levels: soil as a resource, water, and the area used for planting and harvesting the crop. Validation of this model was crucial to ensure that simulation results accurately reflected the reality of the represented system, thus instilling confidence in scenario studies conducted with it. Finally, eight scenarios were established concerning irrigation management and fertilization in the crop, and the simulation results were subsequently evaluated to define policies addressing the studied problem.

3.4. Causal Loop Diagram

Initially, the causal loop diagram presented in Figure 2 was developed. Thirty-nine variables were identified, which make up 20 feedback loops, 9 reinforcement (positive), and 11 compensation (negative). This model is classified into three subsystems: one for water resources, one for soil resources [25], and a subsystem for socioeconomic factors and crop performance [29, 30].

[figure(s) omitted; refer to PDF]

The subsystem of the soil resource, in loops B3, B4, and B5, shows, for example, that agricultural expansion cannot occur excessively, but on the contrary, due to resource limitation [40], its growth ends up balancing, since, with the expansion of a particular crop, it ends up replacing other crops and thus frustrating the adequate rotation. Additionally, it gives rise to the realization of practices such as deforestation or the use of land without agricultural vocation, with which, in the long term, soil loss is caused, decreasing the area available for planting and with this agricultural expansion.

For its part, the water resource subsystem represents the influence of water on crop yields and how it affects the availability of resources [40]. For example, loop B11 indicates that the planting of a crop generates water demand, which depends on its characteristics; therefore, by increasing planting, the water demand will increase, and thus, there will be a decrease in the availability of the resource, which leads to the inability to exercise appropriate irrigation practices and damage crop yields. Under this scenario, the production and availability of food would also decrease, slowing population growth to some extent, and with agricultural expansion, hence the tendency to decrease planting as well.

Finally, the subsystem of socioeconomic factors and crop performance exposes in loops R5 and R6 that, by obtaining greater profits, investment in research and development is encouraged, either through technification and/or mechanization; as a consequence, improvements in crop yields will be generated, which increases agricultural production, and with these sales, revenues and profits will be higher. In parallel, the R7 loop points out that utility increases can mean greater investment in irrigation systems, by which it will be possible to promote more and better irrigation practices that promote an increase in crop yields, thus achieving higher incomes and profits.

3.5. Stock and Flow Diagram

Once the main variables of the system and their interrelationships were defined, the stock and flow diagram were generated (Figure 3); these variables were translated into mathematical equations, which allowed the subsequent simulation and analysis of the model in Vensim software.

[figure(s) omitted; refer to PDF]

This diagram consists of three level variables: soil, planted area, and available surface water, which we used to monitor the status of these factors throughout the simulation period and the influence of the input and output flows related to each one.

These three variables were chosen due to the importance and relevance of knowing the availability of soil and water as a product of the strategies and techniques used in the production of bulb onions, as well as the changes over time in the area planted due to the availability of these natural resources.

In Table 4, the main variables used in the model are described along with their corresponding values, as applicable.

Table 4

Variables in the simulation model.

Variable	Units	Description
Recovery	ha/year	The area of soil being reclaimed per unit of time, and this recovery is due to the use of organic fertilizer.
Recovery time	Year	The time it takes for degraded soil to recover due to the use of compost. It has a value of 60–90 days [41].
Soil	ha	The amount of soil available year by year, and its value increases due to recovery and decreases due to loss of this resource.
Soil loss	ha/year	The area of soil lost per unit of time due to degradation. It corresponds to 26.1% of the degraded area.
Degradation	ha/year	Amount of land with loss of some properties, leading to a decrease in its production capacity.
Erosion	%	Percentage of agricultural land with some degree of erosion. It has a value of 60.4% [42].
Salinization	%	Percentage of land for transitory crops with some degree of salinization. It has a value of 38.6% [43].
Area available to cultivate	ha	The amount of soil that can be used for planting depends on the available soil, the area with agricultural potential, and the currently planted area.
Area with agricultural vocation	ha	The amount of soil with characteristics suitable for establishing bulb onion production systems. For Boyacá, it has a value of 559,997 [44].
Planted area	ha	The amount of land planted year by year. This value increases due to planting and decreases due to harvesting and loss.
Production	Tons per year	Total volume of bulb onion produced.
Fertilizer yield	%	Increase in onion yield due to the addition of magnesium and micronutrients in the fertilizer [45].
Yield	Tons per ha	The amount of bulb onion obtained per hectare harvested. Its value varies according to the irrigation system employed and the fertilizer used.
Sale	Tons per year	Volume of bulb onion sold.
Consumption rate	Tons per person per year	Amount of bulb onion consumed per person per year, nationally. It has a value of 0.0103 [46].
Irrigation system efficiency	%	Irrigation system efficiency, affecting the volume of water to be extracted and the crop yield. It has a value of 50% for gravity, 70% for traditional irrigation, 75% for sprinkler, and 90% for drip irrigation [42].
Water demand bulb onion	m3 per tons	Amount of water required during the entire vegetative cycle of bulb onion. It takes a value of 258 [47].
Total water requirement	m3 per year	Total amount of water required to meet the entire production.
Extraction	m3 per year	Amount of water extracted from available surface water sources to meet the demand.
Surface water available	m3	Volume of available surface water year by year increases due to incoming water flow and decreases due to extraction.
Water flow	m3 per year	Flow of water that feeds into the volume of available surface water, composed of precipitation and water sources.
Water sources	m3 per year	Volume of water available in the upper basin of the Chicamocha River. It ranges from 706.2 to 6318.4 million m3 [42].
Precipitation	m3 per year	Total amount of rainfall in Boyacá annually. It ranges from 4787 to 9574 m3

Authors’ elaboration.

The equations required by the model of the application case are presented in Table 5.

Table 5

Variables and equations by level of the Forrester diagram.

Level	Variable	Units	Equation
Soil	Soil	ha	$$\text{Recovery}-\text{Degradation}-\text{Soil}\text{loss}$$
	Recovery	ha per year	$$\text{MIN}\text{Degradation}\ast 0.9222,\text{Degradation}\ast 0.9222/\text{Recovery}\text{time}$$
	Degradation	ha	$$\text{Erosion}+\text{Salinization}\ast \text{Planted}\text{area}$$
	Soil loss	ha	$$\text{IF}\text{THEN}\text{ELSE}\text{Soil}>=0,\text{MIN}0.261\ast \text{Degradation},\text{Soil},0$$
Planted area	Planted area	ha	$$\text{Sowing}-\text{Harvest}-\text{Loss}$$
	Sowing	ha	$$\text{IF}\text{THEN}\text{ELSE}\text{Area}\text{available}\text{to}\text{cultivate}/\text{year}>=\text{Demand}/\text{Yield},\text{Demand}/\text{Yield},\text{Area}\text{available}\text{to}\text{cultivate}/\text{year}$$
	Area available to cultivate	ha	$$\text{IF}\text{THEN}\text{ELSE}\text{Soil}‐\text{Planted}\text{area}<=\text{Area}\text{with}\text{agricultural}\text{vocation},\text{Soil}‐\text{Planted}\text{area},\text{Area}\text{with}\text{agricultural}\text{vocation}$$
	Loss	ha	$$\text{RANDOM}\text{NORMAL}0,0.4,0.16469,0.13264,0\ast \text{Planted}\text{area}$$
	Harvest	ha	$$\text{Planted}\text{area}/\text{Year}-\text{Loss}$$
	Production	Tons	$$\text{Harvest}\ast \text{Yield}$$
	Yield	Tons per ha	$$\text{Main}\text{yield}\ast \text{Irrigation}\text{system}\text{efficiency}\ast 1+\text{Fertilizer}\text{yield}$$
	Sale	Tons	$$\text{IF}\text{THEN}\text{ELSE}\text{Demand}<\text{Production},\text{Demand},\text{Production}$$
	Income	Million $ per ton	$$\text{Sale}\ast \text{Sale}\text{price}$$
	Utilities	Million $	$$\text{Income}-\text{Cost}$$
	Cost	Million $ per ha	$$\text{Main}\text{cost}\ast \text{Planted}\text{area}$$
Surface water available	Surface water available	m3	$$\text{Water}\text{flow}-\text{Extraction}$$
	Water flow	m3	$$\text{Water}\text{sources}+\text{Precipitation}$$
	Extraction	m3	$$\text{IF}\text{THEN}\text{ELSE}\text{Total}\text{water}\text{requirement}/\text{Irrigation}\text{system}\text{efficiency}<=\text{Surface}\text{water}\text{available}/\text{Year},\text{Total}\text{water}\text{requirement}/\text{Irrigation}\text{system}\text{efficiency},\text{Surface}\text{water}\text{available}/\text{Year}$$
	Total water requirement	m3 per year	$$\text{Water}\text{demand}\text{bulb}\text{onion}\ast \text{Production}$$
	Irrigation system efficiency	%	$$\text{Sprinkling}\ast 0.75+\text{Gravity}\ast 0.5+\text{Dripping}\ast 0.9+\text{Craft}\ast 0.7$$

Authors’ elaboration.

3.6. Model Validation

The structural and behavioural validation of the model was carried out based on the steps proposed by Sterman. For the structural validation, it was found that the diagram of influences described the system studied, and on the other hand, it was verified that the relationships represented in the diagram of influences had coherence with the equations of the Forrester diagram. Finally, the model was simulated, and its behaviour was verified as reasonably representing the real system, as shown in Figure 4.

[figure(s) omitted; refer to PDF]

4. Results and Discussion

To evaluate the availability of soil and water resources, their use, and implications for the development of bulb onion cultivation, different policy scenarios were proposed to understand the long-term dynamic behaviour of these resources and therefore favour decision-making related to their management.

The proposed scenarios seek to study the effect on crop yield due to the transition to a drip irrigation system and, on the other hand, to the application of fertilizer [27] with the addition of magnesium and micronutrients, which is based on the results of the research carried out by [45, 48, 49] and presented in Table 6.

Table 6

Effect of Mg and micronutrients on bulb onion yield.

No.	Treatment	Harvest yield (ton/ha)	Increase in performance (%)
0	NPK	36.29	0.0
1	NPKMg	42.25	16.4
2	NPKMgB	37.29	2.8
3	NPKMgZn	41.27	13.7
4	NPKMgMn	34.76	−4.2
5	NPKMgBZn	36.36	0.2
6	NPKMgBMn	39.69	9.4
7	NPKMgMnZn	51.33	41.4
8	NPKMgBZnMn	54.40	49.9

4.1. Water Resource Use Scenarios

In this order of ideas, four scenarios related to irrigation management were proposed, in which a variation is proposed to the percentage of irrigation that is carried out under the drip method [40], as shown in Table 7.

Table 7

Irrigation management simulation scenarios.

Variable	Base	Scenario 1	Scenario 2	Scenario 3	Scenario 4
Dripping	0.39%	40%	60%	80%	100%

4.2. Land Resource Use Scenarios

Likewise, Table 8 presents the scenarios established to analyse the increases in crop yield as a result of different combinations and additions of magnesium and micronutrients in fertilization.

Table 8

Soil fertilization simulation scenarios.

Variable	Base	Scenario 5	Scenario 6	Scenario 7	Scenario 8
	NPK	NPKMgZn	NPKMg	NPKMgMnZn	NPKMgBZnMn
Fertilizer yield	0.0%	13.7%	16.4%	41.4%	49.9%

4.3. Simulation

Subsequently, the model was simulated for a period of 15 years (between 2019 and 2034) to evaluate the behaviour of the system in each of the scenarios proposed; for each scenario, the results obtained in the variables “yield,” “area available for cultivation,” and “extraction” were compared. The results obtained are shown in Table 9.

Table 9

Simulation results.

Variable	Base	Irrigation management				Fertilization management
		1	2	3	4	5	6	7	8
Yield (ton/ha)	27.79	30.04	31.23	32.42	33.61	31.59	32.34	39.29	41.65
Available area (ha)	16.305	17.538	18.225	18.877	19.523	18.427	18.836	22.679	23.861
Extraction (mill. m3/year)	60.52	59.19	58.24	57.43	56.91	65.89	66.88	72.82	73.14

As observed, Scenario 8 related to fertilizer management involves improvements to the base scenario for the yield variables and areas available for cultivation, since in both cases, a higher average value was obtained; specifically, the yield increased by 49.9% and the available area by 46.34%. This result indicates an increase in crop productivity. Therefore, it can be said that better benefits are achieved by focusing on fertilization management. In other words, thanks to fertilization, an increase in crop yield is achieved so that a greater use of the cultivated area is obtained. On the other hand, the extraction variable obtained its minimum value in Scenario 4 associated with irrigation management; in this scenario, the annual extraction decreased by 5.97% compared to the base scenario, which shows the efficiency of the drip irrigation system.

5. Conclusions

The present work has allowed us to evaluate the incidence of good agricultural practices such as integrated crop management through the management of the irrigation system and the fertilization plan on the yield and productivity of the bulb onion crop. The results suggest that the implementation of an adequate fertilization plan, according to the needs of the crop and the soil, added to the transition to a drip irrigation system, generates higher yields and greater availability of area to cultivate.

Among the main limitations of the study, it is important to mention that the effects of pollution on water sources and its effects on crops have not been considered; this is an issue that should be addressed in future work to improve the analysis and strengthen decision-making processes.

Another important limitation is that the model did not consider what is related to climatic phenomena such as El Niño and La Niña phenomena and the effects that these phenomena would have on crops and water sources associated with them.

It is also important to delve into the harmful effects of pesticides and fertilizers on soils associated with crops. While these elements are considered in the model, it is important to delve deeper into these effects.

The importance of designing strategies and plans for integrated crop management was confirmed and resulted in strengthening agriculture in the country and improving the socioeconomic context of producers, productivity, and competitiveness of the supply chain, while simultaneously tending towards the conservation of natural resources.

Thanks to good practices in both fertilization and irrigation processes, important results are achieved in crop yields. While this is expected, the model has allowed a quantitative approximation of these effects, which facilitates decision-making processes.

For future research, it is advisable to carry out studies considering additional factors, such as the economic evaluation of the implementation of the irrigation system, agricultural expansion, and the effect of crop rotation, in addition to other aspects that influence crop yield, such as climate, phytosanitary factors, and investment in technology and innovation.

Acknowledgments

This study was funded with own resources.

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