How Surface Irrigation Contributes to Climate Change Resilience—A Case Study of Practices in Mexico

Abstract

Climate change has brought increased temperatures and decreased rainfall on a global scale; however, population growth requires greater volumes of water and food each year that must be supplied in one way or another. In Mexico, application efficiencies in gravity irrigation are below 50%. Although in recent years the decision has been made to change to pressurized irrigation systems to increase the efficiency of water use, border or furrow irrigation is still the most widely used in agriculture. In this work, we show that with a methodology developed and applied in these systems, application efficiencies greater than 90% were obtained, while the Water Use Efficiency (WUE) increased by 27, 38 and 47% for the three crops where it was applied: sorghum, barley, and corn, respectively. Irrigation times per hectare and applied irrigation depths decreased by more than 30%, representing increased irrigation efficiencies and WUE. Finally, the water savings obtained can mitigate water scarcity in cities.

1. Introduction

In recent years, an increase in temperatures and reductions in precipitation has been registered, which have caused the water available for cities to become increasingly scarce [1,2]. However, agriculture water occupies the highest percentage of available water, but its application efficiency (<50%) leads to low yields and, consequently, low productivity and WUE [3]. The effect of climate change can reduce water availability by increasing the demand for crops and the population [4], which can generate short-term restrictions on water destined for agriculture [5,6,7] to prioritize cities [8,9].

According to recent statistics and studies, the application efficiencies in the Irrigation Districts in Mexico are less than 45% [10,11,12], just over 5% below the global average [13,14]. This results in low water productivity (kg/m³) compared to pressurized irrigation systems [15,16]. Although support has been given in recent years to upgrade from traditional irrigation to pressurized irrigation systems, there are still several factors that prevent farmers from using these technologies. Some of these limitations are the little government support, the paid trained staff for the operation of the systems, social factors, and unsupervised irrigation, among others [13,17].

The application efficiencies (<50%) obtained in surface irrigation [11,18] are mainly due to the limited knowledge that farmers have about the amount of water that must be supplied to crops to achieve full development (Figure 1). This causes water losses associated with the deficient design and incorrect inflow discharge at the entrance of the furrows or borders [11]. In addition, most of the plots have high slopes. However, in some urban areas with water scarcity, the increase in productivity and the WUE in agriculture are very important to having sustainable and efficient water management [19,20].

Despite the large studies evaluating the impact of climate change on crops [5,7,21,22], proposals to mitigate the water scarcity, increase productivity, and increase WUE without making substantial changes in the way water is provided to crops in the irrigation districts are not studied in detail.

The irrigation system upgrade requires specialized knowledge in different fields, such as hydraulic, soil science, and crop water requirements, among others. Consequently, it is necessary to determine the specific goals before implementing this new technology [23]. The latter consists mainly of replacing open furrows with pipes and, in general, to move from a surface irrigation system to a pressurized one in either of its modalities: dripping or sprinkling. In addition, the installation of flow meters, an electrical network for pumping water, and periodic system maintenance are needed [24,25].

For comparison, the change from surface irrigation (border or furrow) to drip irrigation involves farmers making an initial investment of around USD 1740 /ha. Meanwhile, if they follow the proposal of this work, that initial investment is around USD 100 /ha, which means 94% less than the original.

In the literature, several models can be found involving processes in surface irrigation (advance, storage, and recession) [26,27,28,29]. Nevertheless, its complexity and limitations derive from low practical use and academic or research applications only, leaving aside its practical application due to all required data (inflow discharge, crops, among others) before starting irrigation [11].

Due to the population growth, it is necessary to generate sustainable methodologies of agricultural production that increase the WUE to protect the environment and the public service of water [30,31]. The decreasing precipitation, due to climate change, has generated irrigation strategies based on meteorology (related to crop evapotranspiration) and soil moisture [32], which, combined with drip irrigation, may lead to a higher WUE [33]. However, the most used irrigation method, and also the oldest, in agriculture worldwide is surface irrigation, used in around 90% of agricultural plots [34,35,36]. And high application efficiencies in surface irrigation systems are possible via applying an optimal discharge in the furrow or border entrance [37]. So, in this work, we present a methodology implemented over five years. The results obtained in the crop yield, productivity, and WUE, show that the volumes of water saved can positively impact ecosystems and provide water to the cities that require it to meet the demands of society.

2. Materials and Methods

2.1. Variable Determination

The furrow and border lengths, longitudinal, and transversal slope were measured using a total topography station. Soil texture was determined by the hydrometer method and classified according to the USDA texture triangle [38], the bulk density (ρ_a) by the method of the cylinder of known volume, and the initial water content through a TDR 300 soil moisture meter. The saturated water content was assimilated to the total soil porosity obtained from the relation φ = 1 − ρ_a/ρ_s, where ρ_s is assimilated as the density of quartz equal to 2.65 g/cm³. All these laboratory methods can be consulted in detail in specialized literature [10,11].

2.2. The Irrigation Tests

To verify the irrigation depths that the farmers used traditionally in each of the irrigations in their plots, it was necessary to conduct irrigation tests that consisted of (a) placing small flags or stakes every 20–30 m from the beginning to the end of border or furrow; (b) measuring the moisture content in the soil before starting irrigation; (c) once the water begins to enter through the furrows or borders, the time in which the wavefront reaches each small flag (advance time) is recorded, and when it reaches the end of the border or furrow, the inflow discharge is cut off and the time during which the water depth decreases from the beginning and disappears at each stake or small flag (recession time) is calculated; (d) during all this time the inflow discharge is measured at the entrance with some device to measure flows.

2.3. Calibration and Validation of the Irrigation Test

With the soil characterization data, initial water content, inflow discharge, and the results from the irrigation test (advance and recession time), we proceeded to obtain the characteristic parameters of the infiltration equation that represent the irrigation event. The water movement simulation over the soil surface is described by the Barré de Saint-Venant equations [39]:

$$\frac{\partial \mathrm{A}}{\partial \mathrm{t}}+\frac{\partial \mathrm{Q}}{\partial \mathrm{x}}=\mathrm{W}$$

(1)

$$\frac{\partial \mathrm{Q}}{\partial \mathrm{t}}+\frac{\partial }{\partial \mathrm{x}}\left(\frac{{\mathrm{Q}}^2}{\mathrm{A}}\right)+\mathrm{g}\mathrm{A}\frac{\partial \mathrm{h}}{\partial \mathrm{x}}+\mathrm{g}\mathrm{A}\left({\mathrm{S}}_{\mathrm{f}}-{\mathrm{S}}_0\right)=\mathsf{\beta }\frac{\mathrm{W}\mathrm{Q}}{\mathrm{A}}$$

(2)

where A = A(x,t) is the hydraulic area (m²), t is time (s), Q = Q(x, t) is the inflow discharge (m³/s), W is the infiltrated volume per unit border or furrow length in unit time (m²/s), g is gravity (m/s²), S₀ is the bottom slope of border or furrow (m/m), S_f is the friction slope (m/m) y b = U_IX/U is a dimensionless parameter where U_IX is the projection in the movement direction of the water mass output velocity due to infiltration, and U is the average velocity.

The friction slope was determined by the fractal law of hydraulic resistance [29]:

$$\mathrm{q}=\mathrm{k}\mathsf{\nu }{\left(\frac{{\mathrm{h}}^3\mathrm{J}\mathrm{g}}{{\mathsf{\nu }}^2}\right)}^{\mathrm{d}}$$

(3)

where ν is the coefficient of kinematic viscosity, k is a dimensionless factor that includes the effects of the roughness of the soil surface, and the exponent d is an exponent such that 1/2 ≤ d ≤ 1 in a way that d = 1/2 corresponds to the Chézy turbulent regime and d = 1 to the Poiseuille regime.

This paper uses the kinematic wave model, which considers that in the momentum equation of Saint-Venant (2), the inertia and pressure terms are insignificant relative to friction and gravity.

The equation of Green and Ampt [40] is used in the subsurface flow:

$${\mathrm{V}}_{\mathrm{I}}=\frac{\mathrm{d}\mathrm{I}}{\mathrm{d}\mathrm{t}}={\mathrm{K}}_{\mathrm{s}}\left(1+\frac{\mathrm{h}+{\mathrm{h}}_{\mathrm{f}}}{{\mathrm{z}}_{\mathrm{f}}}\right), \mathrm{I}\left(\mathrm{t}\right)={\mathrm{z}}_{\mathrm{f}}\left(\mathrm{t}\right)\mathsf{\Delta }\mathsf{\theta }$$

(4)

where Δθ = θ_s − θ₀ is the soil moisture deficit, I is the cumulative infiltration volume per unit area of the soil or infiltrated depth, K_s is the saturated hydraulic conductivity, h is the water depth, and h_f is the suction at the wetting front position (z_f).

However, for the plots where a water table over elevation is presented, due to an excessive irrigation depths application that can affect progressive salinization, an infiltration equation that considers the shallow water table is considered and must be coupled to the Saint-Venant equations [41,42]. The equation to be used in this case is the analytical solution of the Richards equation [42]:

$${\mathrm{V}}_{\mathrm{I}}=\frac{\mathrm{d}\mathrm{I}}{\mathrm{d}\mathrm{t}}={\mathrm{K}}_{\mathrm{s}}\left[1+\frac{\mathrm{h}+{\mathrm{h}}_{\mathrm{f}}\left(1-{\mathrm{z}}_{\mathrm{f}}/{\mathrm{P}}_{\mathrm{f}}\right)}{{\mathrm{z}}_{\mathrm{f}}}\right], \mathrm{I}\left(\mathrm{t}\right)={\mathrm{I}}_{\mathrm{M}}\left[1-{\left(1-{\mathrm{z}}_{\mathrm{f}}/{\mathrm{P}}_{\mathrm{f}}\right)}^2\right]$$

(5)

where P_f is the water table depth and I_M = 1/2ΔθP_f is the maximum infiltration.

The optimization process consists of ensuring, in the best way, that the theoretical advance and recession curves reproduce the data measured in the field, using a non-linear optimization algorithm such as Levenberg-Marquardt [43]. As a result of this process, the average characteristic parameters of that soil are obtained: K_s and h_f.

2.4. Analytical Representation for Optimal Discharge Calculation

The optimal discharge to be applied in the next irrigation is a function of the hydrodynamic properties, border or furrow length, water requirements of the plants, soil water constants, and the average parameters of the infiltration equation obtained from the process before (K_s y h_f). The analytical representation of the optimal discharge of irrigation should be calculated as follows [29]:

$${\mathrm{q}}_0={\mathsf{\alpha }}_{\mathrm{u}}{\mathrm{K}}_{\mathrm{s}}\mathrm{L}, {\mathsf{\alpha }}_{\mathrm{u}}=\frac{{\ell }_{\mathrm{n}}}{{\ell }_{\mathrm{n}}-\frac{{\mathrm{S}}^2}{2{\mathrm{K}}_{\mathrm{s}}}\ln \left(1+\frac{2{\mathrm{K}}_{\mathrm{s}}}{{\mathrm{S}}^2}{\ell }_{\mathrm{n}}\right)}$$

(6)

In this equation, it is important to mention that the product K_sL = q_m represents the minimal unitary discharge necessary for the water reaches the furrow or border end, ${\ell }_{\mathrm{n}}$ is the net irrigation depth, and S is the sorptivity of the medium, which is S² = 2K_sh_f(θ_s − θ₀).

2.5. Application Efficiency and WUE

During each irrigation event, the application efficiency (η_A) is measured, which is defined as the relation between the irrigation sheet storage in the radicular zone (${\ell }_{\mathrm{n}}$) and the total irrigation sheet applied (${\ell }_{\mathrm{b}}$) during the irrigation [11]:

$${\mathsf{\eta }}_{\mathrm{A}}=\frac{{\ell }_{\mathrm{n}}}{{\ell }_{\mathrm{b}}}$$

(7)

Finally, at the end of each crop cycle, the WUE is calculated as the relation between the crop yield (kg) per cubic meter of water applied [44]:

$$\mathrm{W}\mathrm{U}\mathrm{E}=\frac{\mathrm{C}\mathrm{r}\mathrm{o}\mathrm{p}\hspace{0.17em}\mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}\left(\mathrm{k}\mathrm{g}\right)}{\hspace{0.17em}\mathrm{W}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}\hspace{0.17em}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\left({\mathrm{m}}^3\right)}$$

(8)

This relation allows us to calculate the economic value of water in specific irrigation. A high value of WUE indicates that we are producing a greater organic content with less quantity of water.

3. Results and Discussion

3.1. Case of Study

The methodology described above has been applied in three Irrigation Districts in Mexico over a total surface of 18,000 ha: Irrigation District 011 Alto Rio Lerma Guanajuato (8000 ha), Irrigation District 023 San Juan del Rio (5000 ha), and Irrigation District 085 La Begoña Guanajuato (5000 ha) during the Autumn–Winter (A-W) and Spring–Summer (S-S) cycles. Nevertheless, in this work, only the results applied to Irrigation District 023 are shown (Figure 2), where the established crops were barley (Hordeum vulgare), sorghum (Sorghum vulgare), and corn (Zea mays).

3.2. Soil Texture

Figure 3 shows the texture classification obtained from the laboratory and classified according to the triangle of textures proposed by the USDA. It is observed that the soil that predominates the most in the region is Loam (21%), followed by Loam Clay Loam (18%). The determination of this soil property allowed us to detect the plots in which the irrigation depths were excessive based on the texture.

3.3. Irrigation Tests

In this work, 579 irrigation tests were done, and the water inflow discharge of the furrow or the border was quantified with a Doppler effect ultrasonic meter. At the end of the test, the applied irrigation depths were calculated. The results show that in 1250 ha, average irrigation depths of 28 to 39 cm were applied, representing an average application efficiency of 43%. In 2500 ha, irrigation sheets of less than 27 cm were applied (η_A = 69%). In the remaining 1250 ha, we have irrigation sheets greater than 40 cm. It is important to mention that in some cases, irrigation depths of 115 cm were measured, which caused low application efficiencies (10 to 40%). The low application efficiencies were directly related to the variability of the inflow discharge applied to the plot, which fluctuated between 3.5 and 162 L/s.

3.4. Parameters Calibration and Irrigation Design

Using the initial water content, bulk density, porosity, length of the furrows or borders, inflow discharged, and the measured times of the irrigation test data, we calibrated the parameters of the Green and Ampt equation (K_s y h_f) with the kinematic wave model, matching the model data with the data measured during the irrigation tests.

The optimal discharge to be applied in the following set was calculated with Equation (6) using the information of the crop phenological stage. This design involved more work for the farmer, who had to be aware of the progress of water in the plot. However, several causes could be detected that caused them not to follow it properly: (a) the payment they receive is based on the surface they can irrigate per day; (b) the irrigation method has been used for several decades; (c) they let the water advance slowly at night so they can sleep and return the next day, among others.

Table 1. Example of irrigation design in 9 different plots.

Test	Condition	Soil Texture	FPS	IT (min)	IDA (cm)	NID (cm)	ηA (%)
1	TI	Loam	19	1170	63.00	12	19
	DI		13	195	12.50	12	96
2	TI	Sandy Loam	18	175	32.26	12	37
	DI		15	90	12.59	12	95
3	TI	Clay	42	297	20.91	12	57
	DI		30	112	11.89	12	91
4	TI	Silty Clay Loam	32	260	16.29	12	74
	DI		27	193	12.09	12	99
5	TI	Clay	30	375	19.60	12	61
	DI		22	234	12.23	12	98
6	TI	Clay	49	1061	26.88	12	45
	DI		20	160	13.70	12	88
7	TI	Clay Loam	24	537	34.23	12	35
	DI		11	103	13.57	12	88
8	TI	Loam	24	487	30.96	12	39
	DI		12	103	13.55	12	89
9	TI	Silty Loam	206	2760	37.67	12	32
	DI		107	579	12.90	12	93

shows the results of 9 applied irrigation designs, three for each established plot in the growth stage in the following order: corn, sorghum, and barley. The table shows the results divided into traditional irrigation (TI) and designed irrigation (DI); each of the above conditions is classified by soil texture, furrow per set (FPS), irrigation time (IT), irrigation depth applied (IDA), net irrigation depth (NID), and application efficiency (η_A).

For example, in test one (corn crop in the growth stage), the farmer opened 19 furrows and applied 63 cm in the first set (η_A = 19%). However, with the optimal discharge calculated, in the next set he only opened 13 furrows and the irrigation time by set decreased 975 min. Finally, the farmer has an irrigation sheet reduction of 50.5 cm and an increase in the application efficiency from 19 to 96%.

3.5. Reduction of Irrigation Times and Irrigation Depths

Figure 4 shows that by correctly measuring the soil variables, carrying out the irrigation tests, calibrating them, and designing the optimal discharge with the analytical formula (Equation (6)) in each of the plots depending on the crop and phenological stage, it is possible to reduce irrigation times over 40%. With this design, the irrigation times decreased by more than half in all cases, and, for example, for the loamy texture plot where they previously irrigated over a time of 87 h per hectare, now the farmer does it in 18 h. This represents a reduction of 69 h and, consequently, increases water savings and application efficiency. The values marked with * in Figure 4 and Figure 5 corresponds to atypical values in the distribution of errors, commonly called “outlier” [45,46].

The decrease in irrigation times was derived from a depth reduction applied in each plot. In Figure 5, in most cases, this saving represented more than 50%. The reduction of the irrigation sheet with the designed irrigation is evident in all textural classes. However, in some cases, such as loamy textured soils, silt loam, and sandy clay, this saving is more noticeable in relation to the others. However, on average, a saving of 15 cm was achieved in each plot where the design was properly applied. The atypical points shown in Figure 5, where depths of 40 to 98 cm were applied, correspond to plots with lengths greater than 300 m. Figure 6 shows the average water levels applied per hectare to the three crops during the evaluation period. The savings were 29, 46, and 44 cm for sorghum, corn, and barley, respectively.

3.6. Water Use Efficiency

The WUE obtained with Equation (8) is shown in Figure 7 calculated by agricultural year, and his value increased due to the following reasons: (a) the recommendations given and the design applied were followed; (b) the farmers were learning to manage the water in the plots; (c) the inflows discharge of the plots were more and more constant, and (d) over the years there was a better experience in the evaluation and design of surface irrigation.

The correct application of this methodology and the design of the optimal discharge allowed an increase from 0.70 kg/m³ to 1.078 kg/m³ in the barley crop, which represented an increase of 54% in the last year of evaluation. In the case of sorghum and corn, increases of 0.31 kg/m³ and 0.59 kg/m³ were obtained, representing an increase of 23% and 43%, respectively.

Figure 8 shows the total water used in the crop cycle to produce one kilogram of biomass. In general, it is observed that over the years, the amount of water used decreased until it reached 0.93, 0.52, and 0.61 m³ for barley, corn, and sorghum, respectively. The proposal presented here helped stop applying 0.99, 0.32, and 0.23 m³ of water to produce the same kilogram of biomass compared to those plots irrigated conventionally (without applying this methodology).

Because of climate change, complemented by agricultural activity and the growth of the population in the cities, they have caused water to be extracted from wells at greater depths each year, which has implied an increase in the cost of electricity, and in most cases, poor quality water extraction [47,48].

In the literature, several articles can be found that recommend changing surface irrigation systems for more modern irrigation systems to increase the WUE [49]. However, installing a pressurized irrigation system results in high investment and maintenance costs, as well as changing the traditional crop for one that can pay the initial investment in the short term [50]. In addition, studies have shown that the changes from a traditional irrigation system to pressurized ones are done by farmers with high levels of education who generally own large plots [49,51]. The small farmers, in most cases, however, lack study information, and it will be difficult for them to adopt these new technologies [50].

There are several studies about the impacts that climate change has had in recent years in the agricultural sector (less water available, lower crop yields, change of varieties of crops, poor irrigation water quality) [5,7,21,52]. However, the vulnerability of small farmers in these scenarios is greater since, without adequate advice, they can abandon their plots because it will not be profitable for them to cultivate them [50]. Nevertheless, in this work, it was shown that if the proposed methodology is followed correctly, high WUE values can be obtained and crop yield increased without changing the traditional irrigation system for others involving costs to the farmer.

4. Conclusions

The methodology used in these five years (evaluation, design, and application) was used with excellent results. It was shown that when it is applied correctly in the plots, the irrigation times decrease, along with the applied irrigation depths. By generally reducing the number of furrows per set regarding irrigation, the water advances faster, and consequently, there is a significant saving in the volume that has not been applied.

The reduction in irrigation times is clear evidence that the applied methodology helps reduce the amount of irrigation depth applied to crops. The quantified savings were shown per irrigation event, but if we multiply it by 3 irrigations, which are the ones that are normally granted to farmers, the savings are much higher. The farmers are comfortable with the new methodology. However, this implies more work and time for the irrigator, which is the main factor in making a true generational change concerning the irrigation method.

Climate change in recent years has shown us that the way of working in the field must change to have sustainable agriculture. And if we do nothing to mitigate the scarce volume of water to produce the same amount of grain, farmers will abandon their land, and we will have more and more problems of food shortages.

Finally, the irrigation depths applied, the productivity, and the WUE are important indicators in areas where population growth demands more water to meet needs, and water availability becomes essential.