Stochastic Frontier Model for the Evaluation of the Sustainability of Urban Gardens in Puebla, Mexico

Abstract

Amid rapid urbanization and persistent food insecurity in Latin America, urban gardens have emerged as sustainable alternatives to conventional agriculture. This study evaluates the technical and economic efficiency of producing four vegetables (lettuce, Swiss chard, spinach, and tomato) in urban and conventional systems in Puebla, Mexico. Using a stochastic frontier model, the analysis integrates key environmental costs, specifically, water-use efficiency and nutrient balance valuation, to assess the sustainability trade-offs. The results show that urban gardens achieve comparable efficiency to conventional systems while reducing water use by up to 66% and optimizing nutrient cycling. These findings support urban agroecological models as viable strategies for local food production and provide actionable insights for municipal policies aimed at enhancing urban food resilience and environmental performance.

1. Introduction

In recent decades, urban gardens have gained increasing popularity as a sustainable alternative to conventional agriculture in significant cities worldwide [1]. According to the Food and Agriculture Organization of the United Nations (FAO), urban gardens provide fresh, healthy, and safe food and contribute to improving the urban environment, air quality, rainwater infiltration, and the conservation of micro-ecosystems for urban wildlife. Additionally, they strengthen the social fabric of communities by promoting participation and knowledge exchange [2,3].

According to Ávila-Sánchez [4], food production in urban gardens has expanded in popularity in recent decades as a sustainable alternative to conventional agriculture. In other words, urban gardens not only provide the local population with safe, fresh, and healthy food but also contribute to resource efficiency and the reduction of environmental impact [5].

The metropolitan area of Puebla offers a distinctive context for examining urban garden systems. Home to approximately 1.69 million people [6], the city has experienced rapid urban expansion, leading to profound shifts in land use and mounting stress on local food networks. Processes of territorial restructuring have altered the urban–rural interface, creating tensions between industrial development and the preservation of agricultural landscapes [7]. Puebla’s diverse climate and 2000 m elevation shape its unique agroclimatic conditions. Moreover, the region’s longstanding agricultural tradition preserves pre-Hispanic farming knowledge, underscoring the importance of integrating ancestral practices into the design of modern urban garden systems.

In Puebla, Mexico, urban gardens have attracted significant interest due to the growing demand for local and sustainable food. This interest has been amplified by rising food prices, increasing food sovereignty concerns, and the removal of agricultural subsidies that have pushed small-scale producers toward alternative systems [8]. Since 2018, Puebla’s municipal government has supported urban gardens through educational programs and community agroecological initiatives [9], resulting in approximately 400 urban gardens throughout the metropolitan area in schools, parks, vacant lots, and rooftops [10].

The Agri-Food and Fisheries Information Service (SIAP) reported in 2021 that there were 73 lettuce producers, 17 Swiss chard producers, 62 spinach producers, and 2050 tomato producers in the metropolitan area, with a notable increase in the self-consumption of these vegetable products. However, precise and updated data on the total number of existing urban gardens remain unavailable [11,12]. To overcome this gap, this study relied on data generated through controlled experimental trials for urban gardens and a structured survey of conventional producers, ensuring empirical consistency across both systems.

Dumont et al. [13] argue that urban gardens’ sustainability depends on the conscious use of local resources, nutrient recycling, biodiversity promotion, and the minimization of external inputs. However, long-term challenges, such as limited availability of green spaces, water scarcity, and dependence on subsidies, persist across many urban contexts [11]. In Puebla, these challenges are compounded by specific regional constraints. Territorial restructuring has displaced agricultural activities and fragmented rural spaces [7], while competition for land use with urban development projects limits access to productive land. Seasonal rainfall patterns affecting water availability and insufficient institutional support for small-scale agriculture further complicate local sustainability efforts. These socioeconomic and geographic factors underscore the need for robust technical, economic, and environmental evaluation frameworks [14,15,16].

Despite increased scholarly attention to urban gardens in Mexico, a critical research gap persists regarding their technical and economic performance, particularly in Puebla. While studies have addressed systems in Mexico City and Guadalajara [9], Puebla’s unique case remains understudied. Most of the literature emphasizes social, cultural, or environmental dimensions, leaving quantitative efficiency analyses underexplored [8,9]. The application of advanced econometric approaches, such as stochastic frontier models, to evaluate productivity and sustainability in urban gardens remains limited, yet is necessary for informed urban planning and agroecological design.

Urban gardens, as part of broader green infrastructure strategies, can moderate urban microclimates and alleviate extreme heat conditions—particularly in fast-growing cities. Empirical evidence suggests that vegetated areas help suppress the urban heat island effect by reducing both the ground and air temperatures while also contributing to thermal comfort and public health outcomes [17,18]. The effectiveness of such mitigation, however, is closely tied to spatial layout and species composition. As noted by Balany et al. [19], densely planted, strategically positioned vegetation tends to deliver more pronounced cooling effects, especially in zones with intense solar exposure and limited shade. In this context, urban gardens serve not only as productive agricultural systems but also as functional components of urban climate adaptation and resilience frameworks.

Accordingly, this research compares the technical and economic efficiency of producing four vegetables (tomato, Swiss chard, lettuce, and spinach) under controlled urban garden conditions versus conventional production. This analysis employs the stochastic frontier econometric model [20,21], incorporating water use and nutrient balance as key environmental variables [22,23,24]. The reference parameters reported in the scientific literature for horticultural production in Puebla are also considered to contextualize the results. To guide the reader, the structure of this article is as follows. Section 2 presents the materials and methods. Section 3 discusses the results. Section 4 outlines the conclusions, and Section 5 offers practical recommendations derived from the findings.

Conceptualization of Sustainability Assessment in Urban Gardens

The sustainability of urban gardens is a complex and multidisciplinary concept encompassing economic, environmental, and social dimensions [25]. According to Gibson [26], sustainability has remained primarily in a purely descriptive phase. Furthermore, the concept’s ambiguity, multidimensionality, and lack of standardized parameters hinder both its evaluation and effectiveness. Additionally, Lovell [27] suggests that socioeconomic aspects must be considered when assessing the sustainability of urban gardens, reinforcing the complexity of their measurement and analysis.

To analyze and evaluate the sustainability of urban gardens, indicators covering economic, social, and environmental dimensions must be considered. These include dependency on external inputs, income, employment, and efficient water use [28,29]. Integrating ecological principles into food production in urban settings promotes a holistic approach to designing and managing agricultural systems that mimic natural processes, enhance biodiversity, and optimize resource efficiency [29].

Building on this need for integrated analysis, sustainability in urban gardens should be assessed using models that account for efficiency in resource use under specific socio-ecological conditions. Stochastic frontier analysis (SFA) is particularly suitable, as it estimates both technical and economic efficiency while capturing variability in agricultural production. Kaur and Garg [30] note that most existing urban sustainability tools lack the precision to evaluate productive efficiency in specific contexts. Guo and Jin [31] emphasize the importance of spatial heterogeneity in shaping sustainability outcomes—an aspect SFA can address by incorporating environmental and contextual variables. Applying this method to urban gardens in Puebla enables a nuanced, context-sensitive analysis that enhances local sustainability strategies.

In this context, the sustainability of urban gardens can be assessed using either partial indicators or composite indices that capture the multidimensional nature of the concept. Although both approaches have advantages and limitations, indices are often preferred for their ability to integrate multiple dimensions of sustainability into a single framework. This approach has been widely recommended in sustainability assessments due to its ability to synthesize complex, multidimensional data into interpretable metrics [32,33] The current challenge lies in identifying the most suitable index for evaluating sustainability in urban gardens [34], recognizing that sustainability cannot be determined in absolute terms, as no single reference value exists [34,35].

Achieving sustainable development in these urban green spaces is a matter of intergenerational equity, posing a challenge to economic efficiency. That is, reducing the amount of natural resources (inputs) required “per unit of satisfaction” (outputs) helps balance the environmental burden on urban ecosystems while ensuring resources for future generations [36,37]. In this context, economic efficiency can be considered a partial guarantee of sustainability in urban gardens. In this regard, Bismarck et al. [38] point out that raw materials’ attributes are linked to production’s economic efficiency. However, obtaining information on sustainability attributes is complex due to their heterogeneous nature [39].

This study assumes economic efficiency to be one of the most robust indicators for evaluating sustainability, as it allows the incorporation of production factors, thereby analyzing the relationship between economic and environmental aspects [26]. Economic efficiency measurement techniques have advanced significantly in recent decades. To operationalize this concept, there are at least two primary methods for its assessment. The first is data envelopment analysis (DEA), and the second is stochastic frontier analysis (SFA). This methodology enables the measurement of resource-use efficiency and its impact on the sustainability of urban gardens [40].

Although both approaches are widely used, DEA offers a non-parametric approach. However, it does not separate inefficiency from statistical noise. In contrast, the stochastic frontier model (SFM) accounts for both inefficiency and random variation, making it more suitable for data from surveys and field experiments. SFM also accommodates environmental variables directly in the frontier function, enhancing its relevance for this study. Recent applications in agriculture confirm its advantage in contexts with heterogeneity and uncertainty [41,42].

Beyond economic efficiency, technical efficiency is another crucial factor in complementing the sustainability assessment of urban gardens, as it evaluates the competitiveness of production systems and determines the most sustainable technical approaches. Additionally, these methods can facilitate comparative assessments across different production systems [43].

A relevant study was conducted by Fabio A. Madau [44], who used the stochastic frontier production model to evaluate the technical efficiency of organic and conventional cereal farms in Italy. The results indicated that, while organic farms face more significant challenges in terms of technical efficiency, they could close the gap with conventional production through more efficient resource utilization. These findings suggest that improving the efficiency of organic farms is essential for increasing their long-term competitiveness and sustainability.

Another example is Sintori et al. [45], who found that farms operating under organic management schemes exhibited higher profitability, productivity, and efficiency indicators. In contrast, conventional farms performed worse than organic operations, particularly regarding profitability and labor productivity. Additionally, Sintori et al. emphasize the importance of proper farmer training to achieve greater efficiency and note the potential negative impact of subsidies on the appropriate management of inputs and resources.

2. Materials and Methods

2.1. Place of Research

The farming systems analyzed in this study are located in two ecologically contrasting regions of Puebla, Mexico, each with distinct biophysical and operational contexts for comparison. Urban farming trials were implemented in the metropolitan area of Puebla City (19°02′ N, 98°11′ W), located at an altitude of 2135 m. This area features a subhumid climate, with mean annual temperatures of 16.6 °C and a total rainfall of 720 mm [46]. In contrast, data on conventional farming practices were collected in the Tecamachalco irrigation district (18°53′ N, 97°44′ W), approximately 50 km southeast of the capital city of Puebla. The climatic conditions in Tecamachalco are comparatively warmer and drier, with mean annual temperatures of 17.8 °C and a mean rainfall close to 650 mm. While both sites have comparable cropping schedules and similar levels of market integration, they differ substantially in land-use intensity, irrigation infrastructure, and agronomic management strategies [47]. The spatial location of the study sites in Puebla is shown in Figure 1.

2.2. Econometric Modeling Framework

This study analyzes stochastic frontiers for four vegetables (tomato, Swiss chard, lettuce, and spinach) in two production systems, with the results standardized on a per-hectare basis. Additionally, we incorporate reference parameters reported in the scientific literature for horticultural production in the state of Puebla [12], applying the Cobb–Douglas production model:

ln(Y_i) = β₀ + β₁ ln(AL_i) + β₂ ln(F_i) + β₃ ln(W_i) + (v_i − u_i)(1)

where:

ln(Y_i) = logarithm of vegetable yield (Swiss chard, spinach, lettuce, tomato Var. ‘Rio Grande’) in kg/ha for producer i.

ln(AL_i) = logarithm of the amount of labor in workdays/ha for producer i.

ln(F_i) = logarithm of the amount of fertilizers in kg/ha for producer i.

ln(W_i) = logarithm of the amount of water in m³/ha for producer i.

(v_i − u_i) = error term composed of two elements:

v_i = random component capturing measurement errors and other factors.

u_i = non-negative random component representing the inefficiency level of producer i, following a truncated normal distribution.

This model is suitable for calculating the elasticities of inputs concerning production, as proposed by Eigenbrod and Gruda [5] and Sanyé-Mengual et al. [23]. It has also been used to evaluate sustainability in urban agriculture [48].

The Cobb–Douglas production function was chosen for its interpretability and empirical consistency in agriculture. It assumes constant elasticities and returns to scale, making it suitable for small samples. Given the structure of this dataset, it allows for a stable estimation of the input–output relationships. Although more flexible forms like the translog were considered, they risk overfitting and multicollinearity. The model’s simplicity ensures clarity in marginal analysis and cross-system comparisons. Previous studies have validated its application in horticulture and urban agriculture settings [44,49], reinforcing its relevance in this context. Thus, its use balances theoretical rigor with methodological feasibility under real-world conditions.

2.3. Population and Sample

The data for this study (conventional agriculture) were obtained from primary information collected through a survey administered to 120 horticultural producers (March and April 2024) in the Tecamachalco–Puebla irrigation region. These producers cultivated tomato, Swiss chard, lettuce, and spinach. The sample was selected using systematic probability sampling from a target population of 2202 registered horticultural producers in the region [12], representing 5.45% of the specific universe. According to Cochran’s formula for large populations, this sample size ensures estimations with a ≤10% margin of error at a 95% confidence level. The questionnaire includes key characteristics of the region’s annual horticultural production, focusing on the main variables related to the production of the four selected vegetables. The survey collected detailed information on critical production variables, including cultivated area (ha), crop yield (kg/ha), planting density (plants/ha), water usage (m³/ha), fertilizer application rates (kg/ha), nutrient contributions (N, P, K, Ca, Mg, S), labor requirements (workdays/ha), etc. It also considers the number of inputs used in production, the number of hectares dedicated to cultivation, total labor (including permanent, temporary, casual workers, family labor, and contract workers), chemical fertilizers (including nitrogen, phosphorus, potassium, and others, measured in kg), machinery (number of tools), and total water consumption per crop.

For the controlled conditions experiment (urban garden), a completely randomized block design was implemented, following the methodology of Johnson-Chappell and Lavalle [50] for urban agriculture, to produce Swiss chard, spinach, lettuce, and ‘Rio Grande’ tomato. Two hundred eighty plants were distributed in 3.5 L plastic pots, with 70 plants per selected vegetable occupying a surface area of 60 m² per treatment. The statistical power obtained was (β = 0.84, α = 0.05) for an 18-plant crop sample size. To control external variables that could affect the experimental results, substrate composition was standardized using a homogenized mixture (70% commercial potting soil, 30% vermicompost) with verified physicochemical properties (pH 6.8 ± 0.2). Environmental conditions were continuously monitored using a GSP-6 temperature and humidity data logger (±0.5 °C, ±5.0% RH accuracy), complemented with regional climate data from CONAGUA [46], NASA POWER Project [51], and Open-Meteo API [52]. Urban garden production used vermicompost, with essential nutrient data including nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur, compared to chemical fertilizers in conventional production. Additionally, controlled manual irrigation was applied based on a fixed growth rate per crop, maintaining substrate moisture between 65–75% of field capacity through gravimetric measurements, following the methodology proposed by Sagardoy and Varela-Ortega [53]. The horticultural reference system used parameters reported in the scientific literature to produce tomato, Swiss chard, lettuce, and spinach under irrigation conditions in the state of Puebla [12]. A full dataset of inputs and parameters—including yields, costs, water use, labor, and sensitivity analysis—is available in the Supplementary Materials (Table S1).

To ensure methodological robustness between the experimental and field conditions, the measurements were standardized to per-hectare equivalents. The experimental area was divided into six blocks to account for potential environmental gradients, with treatments randomized within each block. Statistical tests validated model assumptions through Breusch–Pagan (homoscedasticity) and Jarque–Bera (normality) analyses (Table 1). This approach provides internal and external validity for comparing efficiency indicators between urban and conventional systems, despite the inherent differences between controlled experiments and field conditions.

2.4. Estimation of Nutrient Balance in Physical Terms

The use and replenishment of macronutrients such as nitrogen, phosphorus, and potassium are vital for soil health and vegetable production [54]. Therefore, the nutrient balance is estimated in both physical and economic terms, considering nutrient inputs, outputs, and replenishment in evaluating production efficiency in agricultural systems. This approach can also be applied to an urban production context [55,56]. The extraction coefficient of each nutrient during the production cycle of the four vegetables per cultivated area was calculated using the following formula:

Ext Nut_i,j = ∑ (ce_i,j × Prod_j)(2)

where:

Ext Nut_i,j = total nutrient extraction for vegetables per cultivated area.

i = nutrient extraction (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur).

j = conventional agriculture or urban garden.

ce_i,j = extraction coefficient for each nutrient.

Prod_j = vegetable production per cultivated area.

Subsequently, only the application of fertilizers (conventional agriculture) and vermicompost (urban garden) was considered to determine the total nutrient replenishment, using the following formula:

Rep Nut_i,j = ∑(fert_i,j × susp_j)(3)

where:

Rep Nut_i,j = total nutrient replenishment.

i = nutrients.

j = cultivated area.

fert_i,j = nutrients supplied by fertilization per hectare.

susp_j = surface area in cultivated hectares.

The physical estimation of the nutrient balance is then determined as the difference between nutrient replenishment and nutrient extraction:

Bal Nut_i,j = (Rep Nut_i,j) ₋ (Ext Nut_i,_j)(4)

Finally, the economic valuation of the nutrient balance is calculated using the damage remediation cost method or avoided costs due to nutrient replenishment, using the most common and low-priced fertilizer per nutrient unit. The prices of each element were derived from the current fertilizer prices during the research period (2024), considering the market price per bag, which is the most common unit acquired by producers. In the case of vermicompost, the price per bag of this organic fertilizer was used. Once the economic cost of the nutrient balance was obtained, it was integrated into the cost function to determine the economic efficiency [21].

(5)$C_T={\sum }_{i=1}^n\left(Q_i\times P_i\right)$

where:

C_T = total nutrient replenishment cost.

Q_i = quantity of nutrient i to be replenished.

P_i = price per unit of nutrient i.

n = total number of evaluated nutrients (nitrogen, phosphorus, potassium, etc.).

2.5. Water-Use Efficiency

Stanghellini [57] highlights the importance of water-use efficiency as an essential sustainability factor in greenhouse horticultural production. Optimized control significantly reduces the environmental impact. The tariff used in this analysis was taken from the water service in Puebla, which was recently adjusted [58], amounting to 22.98 MXN (Mexican pesos) per cubic meter (m³) for both production systems.

To determine the total water use, the volume utilized is analyzed based on the productivity rate of each vegetable (Swiss chard, spinach, lettuce, and tomato) in both agricultural systems, considering productivity loss under different scenarios with a hypothetical reduction in water availability. The costs are evaluated under different conditions, with and without water restrictions, using the following formula:

EV = ((P × Q_{with water −} C_{with water}) − (P × Q_{without water} − C_{without water}))/(V_{irrigation water})(6)

where:

EV = economic value of water for agricultural use in both production systems ( MXN/m³)

P = selling price of the crops

Q_{with water} = productivity with sufficient water

Q_{without water} = productivity under water restrictions

C_{with water} = production costs with sufficient water

C_{without water} = production costs under water restrictions

V_{irrigation water} = volume of water used (m³/ha)

This estimation approach builds upon established methodologies for the marginal valuation of productive inputs under constrained and unconstrained resource scenarios. It has been applied in environmental efficiency analyses within stochastic frontier models [22]. In her study on organic coffee systems in Mexico, Alvarado employed the productivity change method originally proposed by Cristeche and Penna [59], comparing net income variations across irrigation scenarios. Following this rationale, our study quantifies the economic return attributable to irrigation water while excluding infrastructure depreciation and subsidy effects.

2.6. Stochastic Frontier Model

Compared to conventional agriculture, the stochastic frontier econometric model is used to evaluate vegetable production in urban gardens [41,49,60]. The production function is specified in Equation (7):

ln(Y_i) = β₀ + β₁ ln(AL_i) + β₂ ln(F_i) + β₃ ln(W_i) + (v_i − u_i)(7)

where:

ln(Y_i) = vegetable production i (kg/ha)

ln(AL_i) = labor used in vegetable i production (workdays/ha)

ln(F_i) = fertilizers applied in vegetable i production (kg/ha)

ln(W_i) = water used in vegetable i production (m³/ha)

v_i = random error

u_i = technical inefficiency term

Technical efficiency (TE) is calculated as the ratio between observed production (Y_i) and estimated production (Ŷ_i), given the level of inputs used [42]:

TE_i = Y_i/Ŷ_i(8)

Subsequently, economic efficiency is calculated based on cost [21]:

C_i = g(Y_i, P_i; α) + η_i;;; i = 1,2,3,…,n(9)

where:

C_i = total production cost

Y_i = obtained production

P_i = input cost

α = parameters of the cost function

η_i = error term, where: η_i = v_i − u_i.

“v_i“ represents a random error component;

“u_i“ represents a non-negative error component, capturing inefficiency.

EE takes values between 0 and 1, where 1 indicates that the effective cost matches the minimum cost.

Finally, allocation efficiency (AE) is calculated using the following equation:

AE = EE/TE(10)

Allocation efficiency (AE) takes a value of 1 when the factor combination is optimal and 0 when it is not. Once the technical, economic, and allocative efficiency indices for urban gardens and conventional agriculture systems are obtained, averages are used to compare which technology exhibits a higher efficiency index, meaning which system is closer to the optimal factor combination under different scenarios [22].

2.7. Estimates of the Environmental Cost Function Using Stochastic Frontiers

This study presents estimates of the environmental cost function using the econometric allocation efficiency (AE) model for both vegetable production systems (urban gardens and conventional agriculture) and considering the reference parameters reported in the scientific literature. The robustness of the efficiency estimates was tested through sensitivity analyses involving ±20% variations in input prices, irrigation water costs, total production costs, and nutrient replacement expenses. Four predefined scenarios were analyzed: (A) without considering environmental costs, (B) considering environmental costs, (C) without environmental costs at conventional and agroecological product prices, and (D) with environmental costs at conventional and agroecological product prices. Environmental costs include the costs related to water use and nutrient balance. The analysis used an average price to avoid seasonal and market price variations. The models used for each scenario are presented below:

Scenario 1:

ln(Y_i) = β₀ + β₁ lnS_i + β₂ lnLA_i + β₃ lnF_i + β₄ lnR_i + e_i(11)

where:

ln(Y_i) = Represents the natural logarithm of the income per hectare of producer i. It is the dependent variable. It indicates the economic productivity that the producer obtains after deducting the production costs (MXN/ha).

lnS_j = Natural logarithm of the cultivated surface per hectare of producer i. This factor indicates how much space is used for cultivation (ha).

lnLA_i = Natural logarithm of the cost of the day’s wage in pesos per hectare of producer i. This factor measures the labor effort applied in monetary terms and how it influences net income. (MXN/ha)

lnF_i = Natural logarithm of the cost of fertilizers in pesos per hectare of producer i. It represents the cost of the chemical or organic inputs used to improve soil fertility and their impact on income (MXN/ha).

lnR_i = Natural logarithm of the adjusted yield in kilograms per hectare for producer i. It indicates the amount of product that was obtained, adjusted for possible losses or improvements, and how it influences profitability (kg/ha).

e_i = Idiosyncratic error. It represents the variations in the dependent variable (Y_i) that are not explained by the independent variables of the model. Specifically, in this production and econometrics model, e_i considers random effects and unobserved influences that may affect the result of each observation.

β₀, β₁, β₂, β₃, β₄ = Estimated coefficients.

In the stochastic frontier model, these coefficients represent the marginal impact of the independent variables on net income (Y_i). They are estimated by maximizing the likelihood of observing the data given the variable values and the inefficiency (U_i) and random error (V_i) components. Each coefficient reflects how the variables affect the maximum possible production (frontier) and the system’s technical efficiency.

Scenario 2:

ln(Y_i) = β₀ + β₁ lnS_i + β₂ lnLA_i + β₃ lnF_i + β₄ lnCNB_i + β₅ lnCW_i + β₆ lnR_i + e_i(12)

where:

ln(Y_i) = natural logarithm of net income in pesos per hectare of producer i (MXN/ha).

lnS_i = natural logarithm of cultivated area in hectares of producer i (ha).

lnLA = natural logarithm of the cost of daily wages per hectare of producer i (MXN/ha).

lnF_i = natural logarithm of the cost of fertilizers per hectare of producer i (MXN/ha).

lnCNB_i = Natural logarithm of the cost of nutrient balance for producer i. This indicator measures the cost associated with the replacement of nutrients extracted by vegetables, considering the use of fertilizers and other inputs necessary to maintain soil fertility. It represents a key environmental cost in agricultural systems (MXN/ha).

lnCW_i = Natural logarithm of the cost of water used per hectare for producer i. This value considers the cost of the water resource based on the volume of water used in production and its contribution to net income. It includes water tariffs and other related costs (MXN/m³).

lnR_i = natural logarithm of the adjusted yield in kilograms per hectare for producer i (kg/ha).

e_i = idiosyncratic error.

Scenario 3:

ln(Y_i) = β₀ + β₁ lnS_i + β₂ lnLA_i + β₃ lnF_i + β₄ lnR_i + e_i(13)

where:

ln(Y_i) = natural logarithm of net income in pesos per hectare for producer i (MXN/ha).

lnS_i = natural logarithm of cultivated area in hectares for producer i (ha).

lnLA = natural logarithm of daily wage cost per hectare for producer i (MXN/ha).

lnF_i = natural logarithm of fertilizer cost per hectare for producer i (MXN/ha).

lnR_i = natural logarithm of adjusted yield in kilograms per hectare for producer i (kg/ha).

e_i = idiosyncratic error.

Scenario 4:

ln(Y_i) = β₀ + β₁ lnS_i + β₂ lnLA_i + β₃ lnF_i + β₄ lnCNB_i + β₅ lnCW_i + β₆ lnR_i +β₇ lnP_Ai + β₈ lnP_ci + e_i(14)

where:

ln(Y_i) = natural logarithm of net income in pesos per hectare of producer i (MXN/ha).

lnS_i = natural logarithm of cultivated area in hectares of producer i (ha).

lnLA = natural logarithm of the cost of daily wages per hectare of producer i (MXN/ha).

lnF_i = natural logarithm of the cost of fertilizers per hectare of producer i (MXN/ha).

lnCNB_i = natural logarithm of the cost of nutrient balance for producer i (MXN/ha).

lnCW_i = natural logarithm of the cost of water used per hectare of producer i (MXN/m³).

lnR_i = natural logarithm of the adjusted yield per hectare for producer i (kg/ha).

lnP_Ai = Natural logarithm of the average price of agroecological products for producer i. This value reflects the price at which products grown using agroecological practices (urban garden) are sold, offering an insight into their economic competitiveness compared to conventional products (MXN/kg).

lnP_Ci = Natural logarithm of the average price of conventional products for producer i. This value represents the price at which products obtained through traditional agricultural practices are sold, functioning as a reference point in the price comparison (MXN/kg).

e_i = idiosyncratic error.

The statistical analysis was performed using RStudio version 2023.06.1 [61]. Stochastic frontier estimations were conducted using the ‘frontier’ package, following the methodological framework established by Coelli et al. [21].

3. Results and Discussion

The analyses show that the data meet the key statistical assumptions: homoscedasticity (Breusch–Pagan test) and normality (Jarque–Bera test), which validates the robustness of the results. In addition, the likelihood ratio reveals statistically significant differences (p < 0.05) for all crops (tomato, chard, spinach, and lettuce), indicating relevant effects (Table 1).

3.1. Economic Assessment of Nutrient Balance in Different Agricultural Systems

The amount of fertilizer applied varied considerably between systems. In the urban garden, the application was uniform (10,204 kg/ha) across all crops, while in conventional agriculture, the amounts ranged between 415 and 772 kg/ha. The reference parameters indicated intermediate values (290 to 850 kg/ha). Studies such as Rashmi et al. [62] have shown that fertilizer efficiency in conventional agriculture can be reduced by nutrient leaching, explaining the need for higher doses (Table 2).

The cost of fertilizer in the urban garden experiment was considerably higher ($71,428.56 MXN/ha per crop) compared to conventional agriculture ($9906.05 MXN to $18,072.52 MXN/ha) and the reference parameters ($3993.30 MXN to $11,704.50 MXN/ha). This indicates that specialized organic fertilizers used in urban gardens incur a significantly higher unit cost. A study by Dsouza et al. [63] indicated that urban gardens rely on biological or controlled-release fertilizers, which improve the sustainability of the production system but increase costs, as observed in the present research.

Regarding total water use, urban gardens show significantly higher efficiency, with a total consumption of 6,409 m³/ha (adding the four crops), compared to 18,800 m³/ha in conventional agriculture. This difference represents a 66% reduction in the total volume of water used for the urban garden, considering the four vegetables. Efficient water use is key to agricultural sustainability, especially in terms of climate change and water scarcity. When analyzing water efficiency in different production systems, it is observed that urban gardens present superior water management compared to conventional agriculture and the reference values reported in the literature (Table 3).

In the case of tomatoes, water consumption in urban gardens (2,755 m³/ha) is considerably lower than in conventional agriculture (6,600 m³/ha) and in the values reported in the literature (6,371 m³/ha). This difference can be attributed to implementing efficient irrigation technologies, such as drip irrigation and hydroponics, which reduce water waste in urban gardens [64]. Additionally, the valuation of water in urban gardens ($130.63 MXN/m³) is significantly higher than in conventional agriculture ($11.48 MXN/m³), suggesting a better optimization of the resource and greater profitability per unit of water used [65].

To clarify the meaning and origin of this figure, the valuation of water use (MXN/m³) was estimated based on the economic return generated per cubic meter of irrigation water, using the net income differential under full and restricted water availability scenarios. Specifically, the model estimates the marginal contribution of water-to-crop profitability, isolating its economic impact from other inputs. Official municipal water tariffs ($22.98 MXN/m³) were used as baseline costs, without including any subsidies, infrastructure amortization, or public funding. Therefore, the high water valuation observed in urban gardens reflects actual economic returns derived from efficient water use under agroecological practices, particularly in systems with high planting density, precise irrigation, and limited resource availability.

The case of chard reinforces this trend. While urban gardens require only 1,286 m³/ha of water, conventional agriculture consumes 4,800 m³/ha, showing a difference of almost four times in resource use. In addition, the valuation of water in urban gardens ($721.19 MXN/m³) is much higher than in conventional agriculture ($4.17 MXN/m³) and the reference values ($5.18 MXN/m³). This may be because urban gardens apply more controlled irrigation systems and cultivate in optimized spaces with high planting density, improving water profitability [66,67].

Regarding spinach and lettuce, the data show a similar pattern. Urban gardens use 1,286 m³/ha and 1,082 m³/ha, respectively, while conventional agriculture demands 3,900 m³/ha for spinach and 3,500 m³/ha for lettuce. The difference in water valuation ($232.73 MXN/m³ for spinach and $201.82 MXN/m³ for lettuce in urban gardens) versus the values in conventional agriculture ($7.18 MXN/m³ and $12.00 MXN/m³, respectively) highlights the economic and ecological advantage of urban systems in the efficient use of water.

These results indicate that urban gardens can play a key role in food security and agricultural sustainability by significantly reducing water consumption while maintaining a high resource valuation, which favors the system’s profitability. Adopting water efficiency strategies and innovative technologies in food production can be a viable solution to mitigate the effects of the water crisis on global agriculture [68,69,70]. Within the context of climate change and water scarcity, it is crucial to consider water-use efficiency, which will determine the viability of agricultural production in the future [71,72].

3.2. Technical and Economic Efficiency and Yield in Different Agricultural Systems

The analysis of technical efficiency (TE), economic efficiency (EE), and yield in urban garden crops and conventional agriculture provides key information on the sustainability and productivity of both systems (Table 4). In the present study, it is observed that the technical efficiency of the urban garden is like conventional agriculture for crops such as chard (0.90 vs. 0.85, a difference of +5.9%) and spinach (0.74 vs. 0.63, a difference of +17.5%), which indicates a better use of resources in the urban garden. However, for crops such as tomatoes, conventional agriculture presents a higher TE of 0.94 compared to 0.80 in the urban garden (a difference of −14.9%), suggesting that, for high-demand crops, intensive systems could have advantages in input optimization [73].

Economic efficiency (EE) plays a key role in determining the productivity and profitability of these production systems. Yield varies significantly between urban gardens and conventional agriculture, considering the similarity of conventional agriculture to the reference parameters (the literature), reflecting the differences in the management of inputs, technologies, and natural resources.

We can observe that, for spinach cultivation, EE was 0.71 in the urban garden versus 0.73 in conventional agriculture (a difference of −2.7%), suggesting that, although production in urban systems is lower, their efficiency in energy use is comparable. In the case of lettuce, EE in urban gardens (0.83) was higher than that of conventional agriculture (0.82, a difference of +1.2%), indicating that urban gardens can better optimize the resources used. This result coincides with recent studies, highlighting the importance of adapting energy efficiency technologies in urban production [74].

Studies have shown that urban gardens can reduce resource consumption by using innovative techniques such as hydroponics and aeroponics [4]. Other authors have highlighted the relevance of urban agriculture for food security in areas of high population density, providing fresh produce and minimizing logistics costs [75].

Regarding yield (kg/ha), technical efficiency patterns can be observed, where conventional agriculture significantly outperforms urban gardens, with notable differences in tomatoes (61,600 vs. 40,111 kg/ha). These differences can be explained by the intensive use of agrochemicals and technology in conventional agriculture, which maximizes production [76]. However, the literature suggests that these high yields may compromise sustainability due to the increased use of water and fertilizers [77].

Previous studies by Badami and Ramankutty [78] have shown that urban gardens can improve their efficiency and performance by implementing agroecological practices, such as precision agriculture, crop rotation, and organic fertilization. The urban garden achieved higher yields for chard and spinach (47,155.56 kg/ha and 20,755.56 kg/ha, respectively) compared to the conventional system (34,208.00 kg/ha and 17,860.00 kg/ha). In addition, these systems offer additional benefits, such as reducing the carbon footprint and improving urban food security [79,80].

3.3. Stochastic Frontier Analysis in Different Agricultural Systems

The stochastic frontier analysis allows for the evaluation of the performance of different production systems in terms of allocative efficiency [39]. The econometric analysis through the four proposed scenarios shows significant patterns in the functioning of the production systems. In the first scenario, urban gardens show notable advantages in allocative efficiency, with differences of +30.5% in tomato, +27.0% in chard, +24.9% in spinach, and +4.9% in lettuce compared to conventional agriculture. The comparison between urban gardens and conventional agriculture, considering reference parameters (Literature), allows for a better understanding of the sustainability and viability of each system (Figure 2).

In Scenario 1 (without environmental costs, mixed prices), the urban garden presents higher allocative efficiencies, reaching a maximum of 1.00 in lettuce and 0.872 in spinach. At the same time, conventional agriculture shows lower values, with 0.613 in tomato and 0.601 in chard. These results coincide with the findings of Mivumbi et al. [81], who reported greater efficiency in agroecological systems due to their lower dependence on external inputs.

In Scenario 2 (with environmental costs, mixed prices), the allocative efficiency of conventional agriculture shows reductions, reaching values of 0.6 in tomato and 0.579 in chard. At the same time, the urban garden maintains higher values, retaining 1.00 in lettuce and 0.877 in spinach. This behavior can be attributed to the increase in environmental costs associated with using fertilizers and pesticides, which coincides with what was reported by Clark and Tilman [82] on the inverse relationship between environmental costs and productive efficiency.

In Scenarios 3 and 4 (with conventional prices), the allocative efficiency of the urban garden shows variations, with values of 0.727 and 0.732 for tomato and 0.591 and 0.601 for chard, respectively. Conventional agriculture, on the other hand, presents values of 0.644 and 0.64 for tomatoes and 0.631 and 0.618 for chard in the exact same scenarios. These variations can be attributed to a greater sensitivity of these crops to fluctuations in costs and prices. Studies by Rodríguez-Izquierdo et al. [83] identified that urban production systems can be more vulnerable to changes in economic inputs.

3.4. Cost–Benefit and Profitability Indices Using Stochastic Frontier in Different Agricultural Systems

The stochastic frontier analysis applied to the cost–benefit (C/R) ratio and profitability indices (PI) in different production systems shows significant differences between the urban garden and conventional agriculture and the reference values in the literature (Table 5). The results show that the urban garden system presents the best C/R ratio and the highest profitability indices in all of the scenarios evaluated.

These findings are consistent with recent studies. For example, Orsini et al. [79] highlight that urban agriculture can be more profitable due to reduced transport costs and a lower use of chemical inputs. Likewise, Siegner et al. [84] emphasize that urban gardens have economic and social advantages that improve food resilience.

We can observe that, in Scenario 1 (without environmental costs and mixed prices), urban gardens present a considerable advantage in terms of profitability, especially for crops such as chard (C/R = 25.64, IR = 2485.20) and tomato (C/R = 8.37, PI = 725.8). These values are far superior to conventional agriculture, where the values are considerably lower (C/R = 3.28 and PI = 228.7 for chard). These results can be attributed to greater efficiency in using inputs, lower operating costs, and better management of water resources in urban environments [79,85].

When environmental costs are incorporated (Scenario 2), urban gardens’ C/R and profitability indices decrease slightly. For example, the chard PI drops to 2342.60 but still far exceeds conventional agriculture (PI = 174.3). This economic resilience indicates that urban gardens have a lower dependence on external inputs and a greater capacity to mitigate environmental impacts, which is consistent with previous studies that highlight PI sustainability and efficiency in the use of water and nutrients [69,77].

In Scenario 3 (without environmental costs and conventional prices), the advantage of urban gardens is drastically reduced. Tomatoes, for example, fall to a C/R of 3.12 and a PI of 212.4, values that are close to those of conventional agriculture (C/R = 4.82, PI = 382.4). This price sensitivity suggests that, without market incentives and price differentiation, the economic viability of urban gardens is compromised, which has been pointed out by Zasada [68] and Teotónio et al. [86] as one of the main limitations for expanding urban agriculture.

Finally, in Scenario 4 (with environmental costs and conventional prices), the downward trend in the profitability of urban gardens is maintained, with a C/R of 2.82 for tomatoes and 3.87 for chard. Despite this, urban gardens show relative advantages for some crops, such as lettuce (C/R = 4.82, PI = 382.3), which maintains a competitive performance compared to conventional agriculture (C/R = 4.18, PI = 318.7). This selective resilience reinforces the idea that some urban crops may be more resilient to changes in environmental costs and market fluctuations [5,78]. Furthermore, the work of Opitz et al. [87] shows that conventional production tends to be less profitable when environmental costs are internalized.

3.5. Net Revenues Using Stochastic Frontiers in Different Agricultural Systems

From an economic perspective, urban gardens are both resource-efficient and highly profitable (Figure 3), especially in urban environments where demand for fresh food is high [88]. In Scenario 1, tomato revenues from urban gardens reach $2,206,111 MXN, outperforming conventional agriculture by $1,232,000 MXN. For chard, the difference is even more pronounced ($5,422,889 MXN vs. $684,160 MXN). These results are consistent with studies that have evaluated the profitability of urban agriculture, showing that economic returns can outperform conventional systems when direct sales strategies and reduced transportation costs are included [79]. In addition, urban gardens can benefit from tax incentives, government subsidies, and community support, increasing PI competitiveness compared to conventional agriculture [66].

In Scenarios 2 and 4, where environmental costs are included, urban gardens maintain high incomes, while conventional agriculture shows a more significant reduction in profitability. This comparative advantage suggests that the environmental costs not considered in conventional agriculture, such as intensive water use and nutrient balance, affect its long-term viability [68].

The dependence upon external inputs and the fluctuations in market prices make conventional agriculture more vulnerable [86]. At the same time, by operating on more minor scales and with direct access to consumers, urban gardens can maintain more excellent financial stability, highlighting the importance of public policies and support mechanisms to promote sustainable production in urban environments [89].

The sharp decline in the C/B ratios for tomatoes—from 8.37 to 3.12 under conventional pricing (Scenario 3, Table 5)—contrasts with Sintori et al. [45], who found consistent organic farm advantages across price scenarios. This suggests the context-specific nature of our results. While the stochastic frontier model accounts for inefficiencies and environmental costs, it cannot fully reflect socio-ecological complexities. Additionally, observer effects and limited representativeness constrain generalizability, as noted by Dios-Palomares and Martínez-Paz [90].

Additionally, urban gardens not only generate direct economic benefits but also have positive impacts on food security and social cohesion [91]. Studies, such as Lovell [92], have shown that urban gardens can increase access to fresh and healthy food, reducing dependence on external supply chains and decreasing the carbon footprint associated with food transportation, which highlights the importance of public policies and support mechanisms to promote sustainable production in urban environments [89].

3.6. Importance and Implications of the Results

The documented water efficiency advantage (66% reduction) and superior economic valuation of urban gardens have significant implications for municipal water policies in water-stressed regions like Puebla. These findings align with FAO’s [65] emphasis on resource conservation for agricultural viability. The nutrient balance efficiency suggests urban gardens can effectively optimize biogeochemical cycles, supporting Clark and Tilman’s [82] assertion that alternative agricultural systems can maintain economic viability while reducing environmental externalities. For urban planners, these results justify integrating productive green spaces into development strategies that simultaneously address food security, ecosystem services, and climate resilience [92].

3.7. Limitations and Recommendations

Despite robust findings, this study faced limitations, including a controlled experimental design that may not fully capture real-world heterogeneity [78]; a temporal constraint to one growing cycle; and reliance on static price assumptions. Future research should expand to include diverse crops beyond the four vegetables examined; scale the analysis to multiple urban contexts with different socio-environmental characteristics [68]; and integrate ecosystem service valuation into the economic analyses [37]. This would strengthen the evidence base for developing context-specific urban agriculture policies that are applicable to Puebla and similar intermediate cities.

Moreover, although this study focused primarily on efficiency metrics, it did not consider complementary ecological functions, such as microclimate regulation. When embedded within green infrastructure systems, urban gardens may contribute to urban cooling and thermal comfort. Future research should integrate indicators of climate adaptation and thermal performance to reflect the multifunctionality of agroecological systems more comprehensively [17,18,19].

Furthermore, to improve model robustness and relevance, future analyses should explore the sensitivity of key economic outcomes to variations in input prices and environmental cost assumptions. External validity should also be tested by comparing findings across urban typologies, accounting for heterogeneity in land use, infrastructure, and irrigation sources. Finally, although the Cobb–Douglas production function provides consistent estimates, testing alternative models, such as trans log or quadratic forms, may help validate the assumption of constant elasticity in urban agricultural systems.

4. Conclusions

While conventional agriculture maintains a yield advantage for tomato cultivation, urban gardens demonstrated superior performance for Swiss chard, suggesting their potential contribution to more sustainable food systems.

The empirical results support the viability of urban gardens as an efficient and resilient alternative, especially in intermediate cities such as Puebla. Urban systems showed significantly greater water-use efficiency (6,409 m³/ha vs. 18,800 m³/ha; p < 0.01) and lower nutrient replacement costs ($102.75 MXN/ha; 95% CI: 98.62–106.88) than conventional systems ($117.00 MXN/ha; 95% CI: 112.85–121.15; p < 0.05). These outcomes indicate clear opportunities for policy design. Reduced dependence on synthetic fertilizers suggests that municipal programs can promote composting initiatives or organic waste valorization, while water efficiency supports the adoption of targeted drip irrigation subsidies for small-scale producers.

Although the internalization of environmental costs reduced gross income in both systems, urban gardens maintained higher profitability under mixed-price conditions, reinforcing their role in diversified, sustainable food strategies. These findings offer statistically supported grounds for integrating agroecological models into local food and climate resilience planning.

Future research should investigate the performance of such systems under broader urban typologies and variable climate scenarios, particularly when scaled across districts or embedded in municipal policy frameworks.

Ultimately, the future of urban agriculture may not lie in the predominance of one system over another, but in the strategic integration of production models tailored to local socio-ecological realities. Combining the yield potential of conventional agriculture with the input-use efficiency of urban gardens can support the creation of adaptive, resilient, and resource-conscious agroecosystems.

For decision-makers, these results provide statistically grounded insights to inform land-use planning, investment in precision irrigation, and the design of environmentally aligned incentive structures, especially in resource-constrained urban contexts.

5. Practical Recommendations

Based on our findings, we propose the following targeted recommendations.

For municipal policymakers, incentivize the adoption of water-efficient technologies and facilitate regulated access to vacant land for agricultural use.

For urban and peri-urban producers, prioritize high-value crops such as Swiss chard, which showed better performance under urban conditions, and implement precision irrigation to enhance water efficiency.

For urban planners, integrate urban gardens into zoning and land-use regulations as multifunctional spaces that promote food security and environmental resilience.

These actions can help scale the benefits of urban agroecological systems, particularly in resource-constrained intermediate cities like Puebla.

Author Contributions

Conceptualization, O.R.-A., M.H.-L. and E.M.-N.; methodology, E.M.-N. and O.R.-A.; software, R.M.P., S.E.S.G. and E.M.-N.; validation, O.R.-A., R.M.P. and R.R.L.; formal analysis, R.R.L., M.H.-L. and O.R.-A.; resources, O.R.-A., M.H.-L. and E.M.-N.; original draft preparation, E.M.-N. and O.R.-A.; writing—review and editing, R.R.L., R.M.P., S.E.S.G. and O.R.-A.; visualization, O.R.-A., E.M.-N., M.H.-L. and S.E.S.G.; supervision, O.R.-A.; project administration, O.R.-A. and E.M.-N.; funding acquisition, O.R.-A. and E.M.-N. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Ethical review and approval were waived for this study under Article 23 of the Reglamento de la Ley General de Salud en Materia de Investigación para la Salud (Regulations of the General Health Law on Health Research, Mexico.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Acknowledgments

Special thanks to CONAHCYT for the scholarship granted to facilitate the realization of this study as part of a doctoral project, to the Postgraduate Program in Environmental Sciences of the Institute of Sciences-BUAP, and finally, to Laboratory 204 of the Center for Agroecology of the Benémerita Universidad Autónoma de Puebla.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 Location of the study areas in the state of Puebla, Mexico. Highlighted regions correspond to the metropolitan area of Puebla and the Tecamachalco district. Base maps include municipal boundaries and hydrographic features.

Figure 2 Stochastic frontier analysis: heatmap of allocative efficiency (EA) of different agricultural systems. Note: darker colors indicate higher efficiency, with values ranging from 0.579 to 1.000.

Figure 3 Stochastic frontier analysis: net income for different scenarios with four agricultural crops.

Table 1

Statistical tests to assess significance, homoscedasticity, and normality in different crops.

Test	Crops	Statistic Value	95% CI	p-Value	Interpretation
Likelihood Ratio	Tomato	8.234	[7.012, 9.456]	0.012 *	Significant
	Swiss chard	6.892	[5.678, 8.106]	0.018 *	Significant
	Spinach	5.456	[4.234, 6.678]	0.028 *	Significant
	Lettuce	7.123	[5.901, 8.345]	0.015 *	Significant
Breusch–Pagan	Tomato	1.892	[1.234, 2.550]	0.169	Homoscedasticity
	Swiss chard	1.934	[1.276, 2.592]	0.165	Homoscedasticity
	Spinach	1.876	[1.218, 2.534]	0.172	Homoscedasticity
	Lettuce	1.912	[1.254, 2.570]	0.168	Homoscedasticity
Jarque–Bera	Tomato	0.876	[0.456, 1.296]	0.645	Normal
	Swiss chard	0.892	[0.472, 1.312]	0.632	Normal
	Spinach	0.864	[0.444, 1.284]	0.649	Normal
	Lettuce	0.883	[0.463, 1.303]	0.638	Normal

Note: * indicates statistical significance at the α = 0.05 level.

Table 2

Efficiency and costs of fertilizer use by production system.

Production System	Crops
	Tomato		Swiss Chard		Spinach		Lettuce
	kg/ha *	MXN/ha +	kg/ha *	MXN/ha +	kg/ha *	MXN/ha +	kg/ha *	MXN/ha +
Urban Garden	10,204	71,428.56	10,204	71,428.56	10,204	71,428.56	10,204	71,428.56
Conventional Agriculture	772	18,072.52	615	14,384.85	490	11,666.90	415	9906.05
Reference Parameters (Literature)	850	11,704.50	410	5645.70	320	4406.40	290	3993.30

Note: * = amount of fertilizer in kg/ha, ⁺ = total fertilizer cost in MXN/kg.

Table 3

Efficiency and costs of water use by production system.

Production System	Crops
	Tomato		Swiss Chard		Spinach		Lettuce
	m3/ha *	MXN/m3 +	m3/ha *	MXN/m3 +	m3/ha *	MXN/m3 +	m3/ha *	MXN/m3 +
Urban Garden	2,755	130.63	1,286	721.19	1,286	232.73	1,082	201.82
Conventional Agriculture	6,600	11.48	4,800	4.17	3,900	7.18	3,500	12.00
Reference Parameters (Literature)	6,371	11.50	4,250	5.18	3,280	9.15	3,750	12.00

Note: * = volume of water used per hectare (m³/ha); ⁺ = Economic valuation of water use (MXN/m³), representing the monetary return generated per cubic meter of water applied.

Table 4

Comparison of technical efficiency (ET), economic efficiency (EE), and yields in different production systems.

Crops	Production System
	Urban Garden			Conventional Agriculture			Reference Parameters (Literature)
	ET	EE	Performance (kg/ha)	ET	EE	Performance (kg/ha)	ET	EE	Performance (kg/ha)
Tomato	0.80	0.82	40,111.11	0.94	0.80	61,600.00	0.91	0.76	39,800.00
Swiss chard	0.90	0.78	47,155.56	0.85	0.78	34,208.00	0.86	0.72	35,000.00
Spinach	0.74	0.71	20,755.56	0.63	0.73	17,860.00	0.71	0.68	20,000.00
Lettuce	0.62	0.83	21,918.52	0.60	0.82	21,100.00	0.92	0.81	32,500.00
p-value	0.04 *	0.01 *	0.02 *	0.03 *	0.05 *	0.03 *	0.04 *	0.03 *	0.03 *

Note: * Indicates statistical significance at level α = 0.05.

Table 5

Stochastic frontier analysis: cost–benefit (C/R) and profitability indices (PI) in different agricultural systems.

Crops/System	Urban Garden		Conventional Agriculture		Reference Parameters (Literature)
	(C/R)	(PI)	(C/R)	(PI)	(C/R)	(PI)
Scenario 1 (without environmental costs, mixed prices)
Tomato	8.37	725.80	4.82	382.40	4.23	323.50
Swiss chard	25.64	2485.20	3.28	228.70	4.12	312.80
Spinach	9.25	824.90	2.42	142.80	3.31	231.50
Lettuce	7.12	612.30	4.56	356.20	7.45	645.80
Scenario 2 (with environmental costs, mixed prices)
Tomato	7.54	654.20	4.15	315.60	3.76	276.40
Swiss chard	23.42	2342.60	2.74	174.30	3.65	265.40
Spinach	8.63	763.10	2.23	123.50	3.04	204.70
Lettuce	6.68	568.40	4.18	318.70	6.94	594.20
Scenario 3 (without environmental costs, conventional prices)
Tomato	3.12	212.40	4.82	382.40	4.23	323.50
Swiss chard	4.15	315.60	3.28	228.70	4.12	312.80
Spinach	3.56	256.80	2.42	142.80	3.31	231.50
Lettuce	5.14	414.50	4.56	356.20	7.45	645.80
Scenario 4 (with environmental costs, conventional prices)
Tomato	2.82	182.60	4.15	315.60	3.76	276.40
Swiss chard	3.87	287.40	2.74	174.30	3.65	265.40
Spinach	3.37	237.20	2.23	123.50	3.04	204.70
Lettuce	4.82	382.30	4.18	318.70	6.94	594.20

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/urbansci9050164/s1: Table S1: General Parameter: Urban Agriculture vs. Conventional Agriculture.

References

1. Govind, M.; Pinheiro, A. Multifunctional urban agriculture and sustainability: A global overview. Sustain. Urban Agric.; 2018; pp. 20-39. [DOI: https://dx.doi.org/10.1201/9781003359425]

2. Food and Agriculture Organization. Agricultura Urbana: Cultivar los Suelos en la Ciudad; FAO: Rome, Italy, 2015.

3. Guitart, D.; Pickering, C.; Byrne, J. Past results and future directions in urban community gardens research. Urban For. Urban Green.; 2012; 11, pp. 364-373. [DOI: https://dx.doi.org/10.1016/j.ufug.2012.06.007]

4. Ávila-Sánchez, H. Agricultura urbana y periurbana: Reconfiguraciones territoriales y potencialidades en torno a los sistemas alimentarios urbanos. Investig. Geográficas; 2019; 98, pp. 1-25. [DOI: https://dx.doi.org/10.14350/rig.59785]

5. Eigenbrod, C.; Gruda, N. Urban vegetable for food security in cities: A review. Agron. Sustain. Dev.; 2015; 35, pp. 483-498. [DOI: https://dx.doi.org/10.1007/s13593-014-0273-y]

6. Instituto Nacional de Estadística y Geografía (INEGI). Censo de Población y Vivienda 2020: Resultados Definitivos. 2020; Available online: https://www.inegi.org.mx/programas/ccpv/2020/default.html#Tabulados (accessed on 27 August 2024).

7. Bernal-Mendoza, H.; Ramírez-Juárez, J.; Estrella-Chulím, N.; Pérez-Avilés, R.; Morett-Sánchez, J.L. Importancia de los territorios rurales en el proceso de reestructuración territorial: El caso de la región metropolitana de la ciudad de Puebla. Econ. Soc. Territ.; 2010; 10, pp. 625-660. [DOI: https://dx.doi.org/10.22136/est002010130]

8. Jaramillo-Villanueva, J.L.; Rahmani, D.; Gil-Roig, J.; Carranza-Cerda, I. Consumer preferences and willingness to pay for organic and locally produced walnuts: A choice experiment approach. Sustainability; 2025; 17, 565. [DOI: https://dx.doi.org/10.3390/su17020565]

9. Moreno Gaytán, S.I. Entre lo comunitario y la escasez: La práctica de la agricultura urbana en la zona oriente del Valle de México. Trace; 2022; 81, pp. 24-47. [DOI: https://dx.doi.org/10.22134/trace.81.2022.807]

10. Gobierno de Puebla. Lineamientos Para el Funcionamiento del Programa de Huertos Familiares Urbanos. 2021; Available online: https://gobiernoabierto.pueblacapital.gob.mx/transparencia_file/sb/77.fracc1/77_01_sb_line_prog_huertos_urbanos.pdf (accessed on 20 September 2024).

11. Gutiérrez Flores, J.J.; Bernal Mendoza, H.; Merçon, J. Agricultura urbana con enfoques del buen vivir y agroecología en la ciudad de Puebla, México. Rev. Agrotec. Cuba; 2015; 39, pp. 17-28.

12. Servicio de Información Agroalimentaria y Pesquera. Estadística de Producción Agrícola. 2021; Available online: http://infosiap.siap.gob.mx/gobmx/datosAbiertos.php (accessed on 12 August 2024).

13. Dumont, A.M.; Vanloqueren, G.; Stassart, P.M.; Baret, P.V. Clarifying the socioeconomic dimensions of agroecology: Between principles and practices. Agroecol. Sustain. Food Syst.; 2016; 40, pp. 24-47. [DOI: https://dx.doi.org/10.1080/21683565.2015.1089967]

14. Bell, S.; Morse, S. Sustainability Indicators: Measuring the Immeasurable?. Earthscan: London, UK, 2008.

15. Xie, Y.; Liu, C.; Chang, S.; Jiang, B. Urban sustainability: Integrating socioeconomic and environmental data for multi-objective assessment. Sustainability; 2022; 14, 9142. [DOI: https://dx.doi.org/10.3390/su14159142]

16. Shamaee, S.H.; Yousefi, H.; Zahedi, R. Assessing urban development indicators for environmental sustainability. Discov. Sustain.; 2024; 5, 341. [DOI: https://dx.doi.org/10.1007/s43621-024-00563-1]

17. Wong, N.H.; Yu, C. Study of green areas and urban heat island in a tropical city. Habitat Int.; 2005; 29, pp. 547-558. [DOI: https://dx.doi.org/10.1016/j.habitatint.2004.04.008]

18. Norton, B.A.; Coutts, A.M.; Livesley, S.J.; Harris, R.J.; Hunter, A.M.; Williams, N.S.G. Planning for cooler cities: A framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landsc. Urban Plan.; 2015; 134, pp. 127-138. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2014.10.018]

19. Balany, F.; Ng, A.W.M.; Muttil, N.; Muthukumaran, S.; Wong, M.S. Green infrastructure as an urban heat island mitigation strategy—A review. Water; 2020; 12, 3577. [DOI: https://dx.doi.org/10.3390/w12123577]

20. Aigner, D.J.; Lovell, C.A.K.; Schmidt, P. Formulation and estimation of stochastic frontier production function models. J. Econom.; 1977; 6, pp. 21-37. [DOI: https://dx.doi.org/10.1016/0304-4076(77)90052-5]

21. Coelli, T.J.; Rao, D.S.P.; O’Donnell, C.J.; Battese, G.E. An introduction to Efficiency and Productivity Analysis; 2nd ed. Springer: New York, NY, USA, 2005; Available online: https://link.springer.com/book/10.1007/b136381 (accessed on 8 May 2024).

22. Alvarado-Boirivant, L.S. Evaluación de la sustentabilidad de la producción orgánica del café a través de la medición de eficiencia económica con variables ambientales. Nat. Econ.; 2013; 1, pp. 91-110. [DOI: https://dx.doi.org/10.21704/ne.v1i2.1424]

23. Sanyé-Mengual, E.; Anguelovski, I.; Oliver-Solà, J.; Montero, J.I.; Rieradevall, J. Resolving differing stakeholder perceptions of urban rooftop farming in Mediterranean cities: Promoting food production as a driver for innovative forms of urban agriculture. Agric. Hum. Values; 2016; 33, pp. 101-120. [DOI: https://dx.doi.org/10.1007/s10460-015-9594-y]

24. Castillo Murcia, Y.D. Análisis de la Eficiencia Técnica Agrícola con el uso de Fronteras Estocásticas de Producción. Master’s Thesis; Pontificia Universidad Javeriana: Bogota, Colombia, 2023.

25. Funsten, C.; Borsellino, V.; Schimmenti, E. A systematic literature review of historic garden management and its economic aspects. Sustainability; 2020; 12, 10679. [DOI: https://dx.doi.org/10.3390/su122410679]

26. Gibson, R.B. Beyond the pillars: Sustainability assessment as a framework for effective integration of social, economic and ecological considerations in significant decision-making. J. Environ. Assess. Policy Manag.; 2006; 8, pp. 259-280. [DOI: https://dx.doi.org/10.1142/S1464333206002517]

27. Lovell, S.T.; Taylor, J.R. Supplying urban ecosystem services through multifunctional green infrastructure in the United States. Landsc. Ecol.; 2013; 28, pp. 1447-1463. [DOI: https://dx.doi.org/10.1007/s10980-013-9912-y]

28. Marten, G.G. Productivity, stability, sustainability, equitability and autonomy as properties for agroecosystem assessment. Agric. Syst.; 1988; 26, pp. 291-316. [DOI: https://dx.doi.org/10.1016/0308-521X(88)90046-7]

29. Gliessman, S.R. Agroecology: The Ecology of Sustainable Food Systems; 3rd ed. CRC Press: Boca Raton, FL, USA, 2014; [DOI: https://dx.doi.org/10.1201/b17881]

30. Kaur, H.; Garg, P. Urban sustainability assessment tools: A review. J. Clean. Prod.; 2019; 210, pp. 146-158. [DOI: https://dx.doi.org/10.1016/j.jclepro.2018.11.009]

31. Guo, X.; Jin, F. Heterogeneity of urban-rural responses to multigoal policy from an efficiency perspective: An empirical study in China. Habitat Int.; 2025; 158, 103341. [DOI: https://dx.doi.org/10.1016/j.habitatint.2025.103341]

32. Singh, R.K.; Murty, H.R.; Gupta, S.K.; Dikshit, A.K. An overview of sustainability assessment methodologies. Ecol. Indic.; 2009; 9, pp. 189-212. [DOI: https://dx.doi.org/10.1016/j.ecolind.2008.05.011]

33. López-Ridaura, S.; van Keulen, H.; van Ittersum, M.K.; Leffelaar, P.A. Multiscale methodological framework to derive criteria and indicators for sustainability evaluation of peasant natural resource management systems. Environ. Dev. Sustain.; 2005; 7, pp. 51-69. [DOI: https://dx.doi.org/10.1007/s10668-003-6976-x]

34. Wezel, A.; Bellon, S.; Doré, T.; Francis, C.; Vallod, D.; David, C. Agroecology as a science, a movement and a practice. A review. Agron. Sustain. Dev.; 2009; 29, pp. 503-515. [DOI: https://dx.doi.org/10.1051/agro/2009004]

35. Altieri, M.A.; Nicholls, C.I. Agroecology and the Search for Truly Sustainable Agriculture;. United Nations Environment Programme: Mexico City, Mexico, 2005.

36. Barrett, C.B. Displaced distortions: Financial market failures and seemingly inefficient resource allocation in low-income rural communities. World Dev.; 2007; 35, pp. 1993-2006. [DOI: https://dx.doi.org/10.1016/j.worlddev.2007.02.006]

37. Farley, J.; Costanza, R. Payments for ecosystem services: From local to global. Ecol. Econ.; 2010; 69, pp. 2060-2068. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2010.06.010]

38. Bismarck, A.; Baltazar-Y-Jimenez, A.; Sarikakis, K. Green composites as panacea? Socio-economic aspects of green materials. Environ. Dev. Sustain.; 2006; 8, pp. 445-463. [DOI: https://dx.doi.org/10.1007/s10668-005-8506-5]

39. De la Fuente, E.B.; Suárez, S.A.; Ghersa, C.M. Soybean weed community composition and richness between 1995 and 2003 in the Rolling Pampas (Argentina). Agric. Ecosyst. Environ.; 2008; 123, pp. 320-326. [DOI: https://dx.doi.org/10.1016/j.agee.2006.01.009]

40. Guerrero, I.R.P.; Gastaldi, L.B.; Gatti, N. Eficiencia técnica de la lechería pampeana. Fronteras estocásticas con heterogeneidad observada y no observada. Cuad. Del C.; 2017; 19, pp. 88-114.

41. Tesema, T.G. Application of stochastic frontier to agriculture in Ethiopia. Appl. Artif. Intell.; 2022; 36, 2062817. [DOI: https://dx.doi.org/10.1080/08839514.2022.2062817]

42. Kumbhakar, S.C.; Wang, H.-J.; Horncastle, A.P. A Practitioner’s Guide to Stochastic Frontier Analysis Using Stata; Cambridge University Press: Cambridge, UK, 2015; [DOI: https://dx.doi.org/10.1017/CBO9781139342070]

43. Toro, P.; García, A.; Gómez-Castro, A.; Perea, J.; Acero, R.; Rodríguez-Estévez, V. Evaluación de la sustentabilidad en agroecosistemas. Arch. De Zootec.; 2010; 59, pp. 71-94. [DOI: https://dx.doi.org/10.21071/az.v59i232.4908]

44. Madau, F.A. Technical efficiency in organic and conventional farming: Evidence from Italian cereal farms. Agric. Econ. Rev.; 2007; 8, pp. 5-21.

45. Sintori, A.; Konstantidelli, V.; Gouta, P.; Tzouramani, I. Profitability, productivity, and technical efficiency of Cretan olive groves across alternative ecological farm types. Agriculture; 2023; 13, 2194. [DOI: https://dx.doi.org/10.3390/agriculture13122194]

46. Comisión Nacional del Agua (CONAGUA). Información Estadística Climatológica. Servicio Meteorológico Nacional. 2022; Available online: https://smn.conagua.gob.mx/es/climatologia/informacion-climatologica/informacion-estadistica-climatologica (accessed on 7 July 2024).

47. Velasco Hernández, M.Á.; Ascencio López, W.J.; Neri-Suárez, M.; Hernández Espinosa, M.Á.; Guevara Espinosa, M.D.; Salgado Villavicencio, E. Extreme drought trends in Puebla: Climate and socioeconomic indices with implications for water management. Agric. Soc. Dev.; 2023; 21, pp. 115-130. [DOI: https://dx.doi.org/10.22231/asyd.v21i1.1627]

48. Meeusen, W.; van den Broeck, J. Efficiency estimation from Cobb-Douglas production functions with composed error. Int. Econ. Rev.; 1977; 18, pp. 435-444. [DOI: https://dx.doi.org/10.2307/2525757]

49. Battese, G.E.; Coelli, T.J. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir. Econ.; 1995; 20, pp. 325-332. [DOI: https://dx.doi.org/10.1007/BF01205442]

50. Johnson-Chappell, M.J.; Lavalle, L. Food security and biodiversity: Can we have both? An agroecological analysis. Agric. Hum. Values; 2011; 28, pp. 3-26. [DOI: https://dx.doi.org/10.1007/s10460-009-9251-4]

51. National Aeronautics and Space Administration. Prediction of Worldwide Energy Resources (POWER) Project. NASA Langley Research Center. n.d. Available online: https://power.larc.nasa.gov/ (accessed on 18 April 2025).

52. Open-Meteo. Open-Meteo: Free Weather APIs for Non-Commercial Use. n.d. Available online: https://open-meteo.com/ (accessed on 18 April 2025).

53. Sagardoy, J.A.; Varela-Ortega, C. Rethinking Water: Present and Future Roles of Water and Food Trade. Water Policy Publications 2023; Available online: https://agua.org.mx/wp-content/uploads/2019/10/libro-completo-rethinking-water-1.pdf (accessed on 9 September 2024).

54. Yahaya, S.M.; Mahmud, A.A.; Abdullahi, M.; Haruna, A. Recent advances in the chemistry of nitrogen, phosphorus and potassium as fertilizers in soil: A review. Pedosphere; 2023; 33, pp. 385-406. [DOI: https://dx.doi.org/10.1016/j.pedsph.2022.07.012]

55. Cabrini, S.M.; Calcaterra, C.P. Costos ambientales y eficiencia productiva en la producción agraria del Partido de Pergamino. Rev. Iberoam. De Econ. Ecol.; 2011; 20, pp. 27-43.

56. Pacheco, F.S.; Ometto, J.P.H.B.; Gomes, L.; Tosto, K.; Miranda, M.; Loverde-Oliveira, S.; Cox, C. Nutrient balance and use efficiency in agricultural lands in the Vermelho river watershed, upper Pantanal, Brazil. J. Geophys. Res. Biogeosciences; 2021; 126, e2020JG005673. [DOI: https://dx.doi.org/10.1029/2020JG005673]

57. Stanghellini, C. Horticultural production in greenhouses: Efficient use of water. Acta Hortic.; 2014; 1034, pp. 25-32. [DOI: https://dx.doi.org/10.17660/ActaHortic.2014.1034.1]

58. Organismo Operador del Servicio de Agua Potable y Alcantarillado del Municipio de Puebla. Acuerdo Tarifario 1° mayo 24. 2024; Available online: https://www.aguapuebla.mx/wp-content/uploads/2024/05/08-Acuerdo-Tarifario-1%C2%B0-mayo-24.pdf (accessed on 26 September 2024).

59. Cristeche, E.; Penna, J.A. Métodos de Valoración Económica de los Servicios Ambientales. Instituto Nacional de Tecnología Agropecuaria 2008; Available online: https://inta.gob.ar/sites/default/files/script-tmp-metodos_doc_03.pdf (accessed on 18 October 2024).

60. Liu, Y.; Myers, R.J. Model selection in stochastic frontier analysis with an application to maize production in Kenya. J. Product. Anal.; 2009; 31, pp. 33-46. [DOI: https://dx.doi.org/10.1007/s11123-008-0111-9]

61. Posit Team. RStudio: Integrated development environment for R (Version 2023.06.1) [Software]. Posit Software, PBC 2023; Available online: https://posit.co/download/rstudio-desktop/ (accessed on 22 August 2024).

62. Rashmi, I.; Shirale, A.; Kartikha, K.S.; Shinogi, K.C.; Meena, B.P.; Kala, S. Leaching of plant nutrients from agricultural lands. Essential Plant Nutrients: Uptake, Use Efficiency, and Management; Naeem, M.; Ansari, A.A.; Gill, S.S. Springer: Cham, Switzerland, 2017; pp. 465-489. [DOI: https://dx.doi.org/10.1007/978-3-319-58841-4_21]

63. Dsouza, A.; Price, G.W.; Dixon, M.; Graham, T. A conceptual framework for incorporation of composting in closed-loop urban controlled environment agriculture. Sustainability; 2021; 13, 2471. [DOI: https://dx.doi.org/10.3390/su13052471]

64. Chartzoulakis, K.; Bertaki, M. Sustainable water management in agriculture under climate change. Agric. Agric. Sci. Procedia; 2015; 4, pp. 88-98. [DOI: https://dx.doi.org/10.1016/j.aaspro.2015.03.011]

65. Food and Agriculture Organization. The State of Food and Agriculture 2021: Making Agrifood Systems More Resilient; FAO: Rome, Italy, 2021; [DOI: https://dx.doi.org/10.4060/cb4476en]

66. Grewal, S.S.; Grewal, P.S.; Islam, K.R.; Smith, J. Can urban agriculture be sustainable?. Agron. Sustain. Dev.; 2011; 31, pp. 291-302. [DOI: https://dx.doi.org/10.1051/agro/2010025]

67. Preite, L.; Solari, F.; Vignali, G. Technologies to optimize the water consumption in agriculture: A systematic review. Sustainability; 2023; 15, 5975. [DOI: https://dx.doi.org/10.3390/su15075975]

68. Zasada, I. Multifunctional peri-urban agriculture—A review of societal demands and the provision of goods and services by farming. Land Use Policy; 2011; 28, pp. 639-648. [DOI: https://dx.doi.org/10.1016/j.landusepol.2011.01.008]

69. Tittonell, P. Ecological intensification of agriculture—Sustainable by nature. Curr. Opin. Environ. Sustain.; 2014; 8, pp. 53-61. [DOI: https://dx.doi.org/10.1016/j.cosust.2014.08.006]

70. Menin, B. Innovative technologies for large-scale water production in arid regions: Strategies for sustainable development. J. Appl. Math. Phys.; 2024; 12, pp. 2506-2558. [DOI: https://dx.doi.org/10.4236/jamp.2024.127150]

71. Food and Agriculture Organization. The State of Food And Agriculture 2022: Leveraging Automation in Agriculture for Transforming Agrifood Systems; FAO: Rome, Italy, 2022; [DOI: https://dx.doi.org/10.4060/cc2459en]

72. Liu, X.; Liu, W.; Tang, Q.; Liu, B.; Wada, Y.; Yang, H. Global agricultural water scarcity assessment incorporating blue and green water availability under future climate change. Earth’s Future; 2022; 10, e2021EF002567. [DOI: https://dx.doi.org/10.1029/2021EF002567]

73. Shah, F.; Wu, W. Soil and crop management strategies to ensure higher crop productivity within sustainable environments. Sustainability; 2019; 11, 1485. [DOI: https://dx.doi.org/10.3390/su11051485]

74. Mohareb, E.; Heller, M.; Novak, P.; Goldstein, B.; Fonoll, X.; Raskin, L. Considerations for reducing food system energy demand while scaling up urban agriculture. Environ. Res. Lett.; 2017; 12, 125004. [DOI: https://dx.doi.org/10.1088/1748-9326/aa889b]

75. Obregón, E.H.; Farelo Castro, J.L.; Buitrago Isaza, R.D. Diseño de un modelo de producción para huertas urbanas. Rev. Vínculos Cienc. Tecnol. Y Sociedad; 2019; 16, 1. [DOI: https://dx.doi.org/10.14483/2322939X.15460]

76. Altieri, M.A.; Nicholls, C.I. Agroecology: A Transdisciplinary, Participatory and Action-Oriented Approach; CRC Press: Boca Raton, FL, USA, 2017.

77. Sanyé-Mengual, E.; Orsini, F.; Gianquinto, G. Revisiting the sustainability concept of urban food production. Environ. Res.; 2015; 34, pp. 142-153. [DOI: https://dx.doi.org/10.1016/j.envres.2014.12.026]

78. Badami, M.G.; Ramankutty, N. Urban agriculture and food security: A critique based on an assessment of urban land constraints. Glob. Food Secur.; 2015; 4, pp. 8-15. [DOI: https://dx.doi.org/10.1016/j.gfs.2014.10.003]

79. Orsini, F.; Kahane, R.; Nono-Womdim, R.; Gianquinto, G. Urban agriculture in the developing world: A review. Agron. Sustain. Dev.; 2013; 33, pp. 695-720. [DOI: https://dx.doi.org/10.1007/s13593-013-0143-z]

80. Food and Agriculture Organization. Los 10 Elementos de la Agroecología; FAO: Rome, Italy, 2019.

81. Mivumbi, M.; Yuan, X. Sustainable environmental economics in farmers’ production factors via irrigation resources utilization using technical efficiency and allocative efficiency. Sustainability; 2023; 15, 4101. [DOI: https://dx.doi.org/10.3390/su15054101]

82. Clark, M.; Tilman, D. Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environ. Res. Lett.; 2017; 12, 064016. [DOI: https://dx.doi.org/10.1088/1748-9326/aa6cd5]

83. Rodríguez-Izquierdo, L.; Rodríguez-Jiménez, S.L.; Macías-Figueroa, O.L.; Benavides-Martell, B.; Amaya-Martínez, O.; Perdomo-Pujol, R.; Miyares-Rodríguez, Y. Evaluación de la producción de alimentos y energía en fincas agropecuarias de la provincia Matanzas, Cuba. Pastos Forrajes; 2017; 40, pp. 222-229.

84. Siegner, A.; Sowerwine, J.; Acey, C. Does urban agriculture improve food security? Examining the nexus of food access and distribution of urban produced foods in the United States: A systematic review. Sustainability; 2018; 10, 2988. [DOI: https://dx.doi.org/10.3390/su10092988]

85. Grewal, S.S.; Grewal, P.S. Can cities become self-reliant in food?. Cities; 2012; 29, pp. 1-11. [DOI: https://dx.doi.org/10.1016/j.cities.2011.06.003]

86. Teotónio, I.; Matos Silva, C.; Oliveira Cruz, C. Eco-solutions for urban environments regeneration: The economic value of green roofs. J. Clean. Prod.; 2018; 199, pp. 121-135. [DOI: https://dx.doi.org/10.1016/j.jclepro.2018.07.084]

87. Opitz, I.; Berges, R.; Piorr, A.; Krikser, T. Contributing to food security in urban areas: Differences between urban agriculture and peri-urban agriculture in the Global North. Agric. Hum. Values; 2016; 33, pp. 341-358. [DOI: https://dx.doi.org/10.1007/s10460-015-9610-2]

88. McClintock, N. Radical, reformist, and garden-variety neoliberal: Coming to terms with urban agriculture’s contradictions. Local Environ.; 2014; 19, pp. 147-171. [DOI: https://dx.doi.org/10.1080/13549839.2012.752797]

89. Postel, S. Urban Farms Now Produce 1/5 of the World’s Food. National Geographic 2014; Available online: https://blog.nationalgeographic.org/2014/05/05/urban-farms-now-produce-15-of-the-worlds-food/ (accessed on 9 September 2024).

90. Dios-Palomares, R.; Martínez-Paz, J.M. Technical, quality and environmental efficiency of the olive oil industry. Food Policy; 2011; 36, pp. 526-534. [DOI: https://dx.doi.org/10.1016/j.foodpol.2011.04.001]

91. Galhena, D.H.; Freed, R.; Maredia, K.M. Home gardens: A promising approach to enhance household food security and wellbeing. Agric. Food Secur.; 2013; 2, 13. [DOI: https://dx.doi.org/10.1186/2048-7010-2-8]

92. Lovell, S.T. Multifunctional urban agriculture for sustainable land use planning in the United States. Sustainability; 2010; 2, pp. 2499-2522. [DOI: https://dx.doi.org/10.3390/su2082499]