Definition 1. An ordered weighed average (OWA) operator (Yager, 1988) of dimension n is a mapping of FOWA:Rn→R that has an associated weighting vector W = [ω₁, ω₂, …, ω_n], such that ω_i ∈ [0, 1] and ∑i=1nωi=1 defined as:

where b_j is the j^th largest of the a_i.

The OWA operator is a non-linear function of elements, since it implies an ordering process. It presents the properties of commutativity, monotonicity, and boundedness:

An immediate application of boundness is idempotency: if a_j = a for all j, then F(a_i, ..., a_n) = a.

Definition 2. A Probabilistic OWA operator (POWA) of dimension n (Merigó, 2008, 2009) is a mapping of FPOWA:Rn→R with two associated weighting vectors W and V of dimension n, such that ω_j and v_i ∈ [0, 1] and ∑j=1nωj=1 and ∑i=1nvi=1 :

where b_j is the j^th largest of the a_i, each argument a_i has an associated weight (probability) v_i, ν^j=δωj+(1-δ)νj with δ ∈ [0, 1], and ν_j is the weight (probability), with ν_i ordered according to b_j, that is, according to the j^th largest of the a_i. If δ = 0 or ω_j = 1/n for all the b_j, a probabilistic mean is obtained; on the contrary, if δ = 1 o v_j = 1/n for all the b_j, the OWA operator is obtained.

The induced ordered weighted average operator (IOWA) introduced by Yager and Filev (1999) uses a second variable, the induced variable, to perform the ordering as a prior step to its aggregation.

Definition 3. An IOWA operator of dimension n is a mapping of FIOWA:Rn×Rn→R; it has an associated weighting vector W of dimension n , such that ω_i ∈ [0, 1] and ∑i=1nωi=1 :

Where b_j is the a_i value of the IOWA pair 〈u_i, a_i〉 having the j^th largest u_i, u_i is the order inducing variable, and a_i is the argument variable.

The induced probabilistic ordered weighted average (IPOWA) is an aggregation operator that uses probability and the OWA operator. Thus, the reordering of the values is performed according to the induced variable that represents a complex process of reordering of the individual distances formed by comparing two sets (Merigó and Casanovas, 2010). This contribution is interesting, as it combines the possibility of occurrence of certain results and the attitudinal character of decision-makers, who assess using inductive variables in order to represent their attitude completely. For this, it considers aspects such as the degree of optimism, psychological aspects, or the pressure of time.

Definition 4. An IPOWA operator (Merigó, 2014) of dimension n is a mapping of FIPOWA:Rn×Rn→R that has the associated weighting vectors W and of dimension n, such that ω_j and υ_i ∈ [0, 1] and ∑j=1nωj=1 and ∑i=1nυi=1:

Where b_j is the a_i value of the IOWA pair 〈u_i, a_i〉 having the j^th largest u_i, u_i is the order inducing variable, each argument a_i is the argument variable with an associated weight (probability) v_i, v^j=δωj+(1-δ)vj with δ ∈ [0, 1], and v_j is the weight (probability), with v_i ordered according to b_j, that is, according to the j^th largest of the u_i.

In this section, some previous concepts, such as the OWA-variance and the OWA-covariance, are analyzed and the Pearson-OWA operator is proposed. These elements will allow for the development of a new methodology named OWA-CRITIC, which allows the ordering of different alternatives, based on a multicriteria system, and the introduction of the possibility that Pearson's correlation coefficient is obtained based on a reordering of the elements to which weights are assigned, not the element itself, but to the position they occupy in the set.

Definition 5. Pearson's correlation coefficient measures the linear relationship between two variables A = {a₁, …, a_n} and B = {b₁, …, b_n} whose means are μ_a and μ_b, respectively. Each argument (a_i − μ_a)(b_i − μ_b) has an associated weight v_i with ∑i=1nvi=1 and v_i ∈ [0, 1], each argument (ai-μa)2 has an associated weight νia with ∑i=1nνia=1 and νia∈[0,1], each argument (bi-μb)2 has an associated weight vib with ∑i=1nvib=1 and vib∈[0,1], and can be defined as FPearson(〈a1,b1〉,…,〈an,bn〉)=∑i=1nvi(ai-μa)(bi-μb)∑i=1nvia(ai-μa)2∑i=1nvib(bi-μb)2. For the case in which vi=via=vib=1/n for all the i, we obtain

Definition 6. The variance OWA operator (Yager, 1996b) of dimension n is a mapping of FVar-OWA:Rn→R that has an associated weighting vector W = [ω₁, ω₂, ..., ω_n], such that ω_i ∈ [0, 1] and defined as

where D_j is the j^th largest of the (ai-μ)2, a_i is the argument variable, and μ is the average (in this case, the OWA operator).

The Var-OWA accomplishes properties similar to other OWA operators, including commutativity, monotonicity, and boundedness. When considering, it becomes the classical variance.

Definition 7. The covariance OWA operator (Merigó, 2011) of dimension n is a mapping of FCovar-OWA:Rn×Rn→R that has an associated weighting vector, such that and defined as

where K_j is the j^th largest of the (a_i − μ_a)(b_i − μ_b), a_i is the argument variable of the first set of elements A = {a₁, …, a_n}, b_i is the argument variable of the second set of elements B = {b₁, …, b_n}, and μ_a and μ_b are the mean of the sets A and B, respectively.

The Covar-OWA accomplishes properties similar to other OWA operators, including commutativity, monotonicity, and boundedness. When considering ω_i = 1/n, it becomes the classical covariance.

The OWA operator can also be implemented into Pearson's correlation coefficient. The use of Pearson-OWA is proposed next, which allows modifying the process of aggregation of the squares of the differences with respect to the mean, and the products of the differences with respect to the means of the two variables to be compared, assigning a higher or lower importance as a function of the degree of optimism or pessimism of the decision-maker.

Definition 8. Pearson-OWA of dimension n is a mapping of FPearson-OWA:Rn×Rn→R that has an associated weighting vector W = [ω₁, ω₂, ..., ω_n], such that and ∑i=1nωi=1 defined as:

Where a_j and b_j are the j^th largest arguments in the sets of elements A = {a₁, ..., a_n} and B = {b₁, ..., b_n}, and μ_a and μ_b are the mean of the sets A and B, respectively. When considering ω_i = 1/n, it becomes the classical covariance.

The Pearson-OWA accomplishes the properties of the OWA operators: symmetry and boundedness, but not monotonicity.

Some special cases are as follows: if ω_j = 0, ∀j ≠ k y ω_k = 1, we obtain the maximum as an absolute value, that is, the maximum or the minimum, and if ω_j = 1/n, ∀j we have the traditional Pearson's coefficient.

The coefficient F_{Pearson−OWA} can be calculated in different ways depending on whether the OWA operator is considered (Table 1). For the study of the OWA variance and the OWA covariance, please see Yager (1996b, 2006). The study can be completed using different types of OWA, such as the maximum [ω = (1, 0, ..., 0)], the minimum [ω = (0, ..., 0, 1)], or the arithmetic mean [ω = (1/n, ..., 1/n)] where n is the number of elements of each variable, as well as weights that only depend on the values of the variables, or additive neat OWA ω = (ω₁, …, ω_n), where ω1=f(xi)/∑i=1nf(xi).

On some occasions, aside from the attitudinal character of the decision-maker, which is introduced through the use of the OWA, objective information is available about the possibility of the occurrence of certain results, or the probability of application of the results, so that they will have to be used. Pearson-POWA is proposed, as it allows combining both types of information.

Definition 9. The Pearson-POWA of dimension n is a mapping of FPearson-POWA:Rn×Rn→R that has an associated weighting vector W of dimension n, such that ω_i and ∑i=1nωi=1:

Where a_j and b_j are the j^th largest arguments in the sets of elements A = {a₁, …, a_n} and B = {b₁, …, b_n}, and μ_a and μ_b are the mean of the sets A and B, respectively. The arguments (aj-μa)(bj-μb),(aj-μa)2, and (bj-μa)2 have an associated weight (probability) v_i ordered according to (aj-μa)(bj-μb),(aj-μa)2, and (bj-μa)2, respectively, ν^j=δωj+(1-δ)vj with δ ∈ [0, 1]. If δ = 0 or ω_j = 1/n for all the (aj-μa)(bj-μb),(aj-μa)2 and (bj-μa)2, the Pearson probability is obtained, in which the weight of each sum is given by the assigned probabilities, while if δ = 1 or v_j = 1/n for all the (aj-μa)(bj-μb),(aj-μa)2, and (bj-μa)2, the F_Pearson-OWA operator is obtained.

Pearson-IPOWA combines the concept of the probability of occurrence or applicability of specific events with the ordering of elements according to the attitudinal character of the decision maker, but in this case, this ordering is performed based on inducting variables of order, which can represent a broad range of aspects, such as the degree of optimism or pessimism, or psychological or time pressure aspects.

Definition 10. The Pearson-IPOWA of dimension n is a mapping of FPearson-IPOWA:Rn×Rn×Rn→R that has an associated weighting vector W of dimension n, such that ω_i ∈ [0, 1] and ∑i=1nωi=1, defined as:

Where D_j, K_j, H_j are the values of (aj-μa)(bj-μb),(aj-μa)2, and (bj-μa)2- respectively, having the j^th largest u_i, u_i is the order inducing variable. Each argument, D_i, K_i, and H_i, has an associated weight (probability) v_i ordered according to the largest of the ui,v^j=δωj+(1-δ)vj with δ ∈ [0, 1], and v_j is the weight (probability); v_i is ordered according to D_j, that is, according to the j th largest of the u_i. If δ = 0 o ω_j = 1/n for all the D_j, K_j, H_j, the Pearson probability F_Pearson-POWA is obtained, on the other hand, if δ = 1 or v_j = 1/n for all the D_j, K_j, H_j the induced Pearson-OWA, F_Pearson-IOWA is obtained.

The following section proposes diverse extensions of the CRITIC methodology, using the operator proposed in the previous section, to obtain the correlation between the values of the different criteria and the use of OWA to obtain the multicriteria score.

The CRITIC methodology uses the multicriteria system to order a set of alternatives. This study is an extension of the proposal by Diakoulaki et al. (1995). The objective is to order a set of alternatives A = {1, ..., |I|}, with cardinality |I|, and according to a set of criteria C = {1, ..., |J|} with cardinality |J|. The following steps are proposed (Figure 1):

Step 1. The generic criteria-alternatives matrix shows the m_ij values of alternative i for criteria j (Table 2).

Step 2. Transformation of the generic criteria-alternatives matrix into the relative score or closeness matrix to the ideal values [x_ij]. For each j criterion, the minimum is obtained, mini(mij), as well as the maximum maxi(mij) of all the alternatives, in a similar manner to the crisp case. When the ideal value is the maximum value (benefits), the closeness to the ideal value is obtained as:

and the ideal value is the minimum value (cost):

Step 3. Obtaining the OWA standard deviation of criterion j, for each vector x_j = (x_1j, x_2j, ..., x_|I|j) of the transformed matrix, from Equation 6

Where X_hj is the h^th largest value of (xij-μj)2,ωi∈[0,1], and ∑i=1nωi=1.

Step 4. Construction of the conflict of dimensions matrix |J| × |J|, whose generic term F_{Pearson-IPOWA j, k} represents the correlation between elements x_j and x_k. Therefore, the weaker the relationship between the criteria j and k, the smallest the value of F_{Pearson-IPOWAj, k} will be.

where Z_{h, k}, V_{h, j}, and W_{h, k} are the values of (xij-μxj)(yik-μyk),(xij-μxj)2, and (xik-μxk)2, respectively, having the h^th largest u_i, with u_i being the order inducing variable, and with μ_xj being the mean of the first set of values for criterion j, X_j = {x_1j, …, x_nj}, and μ_{y, k} the mean of the second set, X_k = {x_1k, …, x_nk}. Each Z_{i, k}, V_{i, j}, and W_{i, k} has an associated weight (probability) v_i with ∑i=1nvi and vi∈[0,1],v^h=δωh+(1-δ)vh with δ ∈ [0, 1] and v_h is the weight (probability), with v_i ordered according to the h^th largest u_i, with ui=∑j=1|J|xij, that is, the sum of all the relative scores of the alternative A_i. This probability represents the probability of occurrence, or in this particular case, the probability of commercially implementing this treatment. This coefficient will be obtained from the opinions of the experts related with the present project.

Step 5. Quantity of information C − OWA_j is emitted by the j^th criterion, which represents the quantity of information transmitted by said criterion, so that the more information transmitted, the greater the value of C − OWA_j:

Step 6. The weight of criterion j is obtained by normalizing the values of C − OWA_j.

Step 7. Obtaining the multicriteria score for alternative i: For this step, four alternatives are proposed depending on how the OWA is used for the relative scores, the weights, for both, or none of them.

7.1. Multicriteria score with IPOWA-CRITIC to order the different alternatives. The Multicriteria score with IPOWA-CRITIC (D_i) is obtained as the product of the weights obtained in Equation 16 by the relative scores obtained in Equations 11, 12.

7.2. Multicriteria score with the IPOWA-OWA-S-CRITIC to order the different alternatives. The IPOWA-OWA-S-CRITIC multicriteria score for alternative i is a mapping of FIPOWA-OWA-S-CRIIC:Rn→R that has the weighted vector obtained in Equation 16 associated: W = [ω₁, ω₂, …, ω_1|J|] where ω_i ∈ [0, 1] and ∑i=1nωi=1 is defined as:

where z_ih is the h^th largest of the x_ij (alternative A_i), and ω_h is the weight obtained in Equation 16.

7.3. Multicriteria score with IPOWA-OWA-W-CRITIC to order the different alternatives. The IPOWA-OWA-S-CRITIC multicriteria score for alternative i is a mapping of FIPOWA-OWA-W-CRITIC:Rn→R that has the weighted vector obtained in Equation 16 associated: W = [ω₁, ω₂, ..., ω_|J|], where and ∑i=1nωi=1 is defined as:

where ψ_h is the h^th largest of the ω_j, and x_ih is the relative score obtained in Equations 11, 12.

7.4. Multicriteria score with IPOWA-OWA-S-W-CRITIC to order the different alternatives. The IPOWA-OWA-S-CRITIC multicriteria score for alternative i is a mapping of FIPOWA-OWA-S-W-CRITIC:Rn→R that has the weighted vector obtained in Equation 16 associated: W = [ω₁, ω₂, ..., ω_|J|], where ω_i ∈ [0, 1] and ∑i=1nωi=1 is defined as:

where ψ_h is the h^th largest of the ω_j and z_ih is the h^th largest of the (alternative A_i) obtained in Equations 11, 12.

Muchamiel tomatoes were grown in a multi-span mesh greenhouse (windbreak greenhouse) that was 26 m wide, 36 m long, and 4 m high until the gutter and 5 m to the ridge, located at the CIAGRO-UMH (Orihuela, Alicante, Spain, Latitude: 38° 05' 05” North; Longitude: 0° 56' 38” West). A short spring-summer cycle was used for 2 consecutive years. The first with a transplantation on 6^th March 2023 and harvesting of plants on 28^th June 2023. The second is between 4^th March and 28^th June 2024. Inside the greenhouse, three plots with different shading systems were set up: (i) without shade (W), (ii) with a fixed and conventional shade nets with 50% of reflection of the solar radiation (F), and (iii) a mobile mesh with photovoltaic shading (P). In each of the plots and on the exterior of the greenhouse, the mean values of the main climate variables were recorded at 10-min intervals, such as ambient temperature and humidity, as well as the intensity of the solar radiation. The mean values of the energy variables were also determined, related to the photovoltaic mesh, at 10-min intervals. In each plot, grafted plants (G) and non-grafted plants (N) were used, with two watering events, according to the needs of the crop, through Allen et al. (2006) method (complete irrigation, C), and with deficit irrigation at 60% of said value (D). The total number of plants used in each treatment was 36 plants. Agroecological strategies and techniques were followed during the management of the crop, such as the application of biostimulants and integrated pest management. Table 3 shows the treatments applied during the assay.

To establish the sustainability of the production strategies used, criteria related to the agronomical, physiological, and biochemical responses of the plant were used, such as production (kg ha⁻¹) and quality, determined starting with the maturity index (°Brix/Acidity) and nutritional composition (%). The profit obtained (€ ha⁻¹) with respect to the economic sustainability of the treatments was also considered. The water consumption (m³ ha⁻¹) and CO₂ fixation (t ha⁻¹) allowed us to consider the environmental sustainability of the treatments. In addition, the social effect was determined starting from the labor used in each treatment. Table 4 shows the criteria used.

Five experts on the subject were consulted to establish confidence in the results obtained in each of the criteria for each of the treatments utilized, and to support the making of decisions on its possible application at a commercial exploitation scale.

To improve the analysis of the sustainability of the agronomic strategies utilized, a sustainability index calculated from the IPOWA CRITIC assignment is proposed. The value of this index varies between 0 and 1. A value of 0 corresponds to the treatment with the worst results according to the criteria used (Table 4), and a value of 1 indicates the best treatment.

The results were statistically evaluated using an analysis of variance, ANOVA, with a 95% confidence interval. The differences between the means of the treatments were analyzed using the least significant difference test of Fisher (LSD) at a probability level of 95%. Significance levels were expressed as: ^* p < 0.05; ^** p < 0.01; ^*** p < 0.001; NS not significant. The results of all treatments were analyzed in each of the 2 years of cultivation, and no significant differences were found between them. The results for the 1^st year of cultivation are presented here.

In order to validate the proposed model, we proceeded to compare the results obtained by applying IPOWA-CRITIC, aside from IPOWA-S-CRITIC, IPOWA-W-CRITIC, and IPOWA-S-W-CRITIC, with other methodologies such as CRITIC, and other selected due to their simplicity, rationality, comprehensibility, good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form, such as the scoring methods: Simple additive weighting, SAW (Podvezko, 2011), and Complex Proportional Assessment, COPRAS, based on the evaluation of different alternative through basic arithmetic operations, adding each normalized value of each criterion by its corresponding weight; and distance-based methods: Multicriteria optimization and compromise solution, VIKOR (Opricovic and Tzeng, 2004), and Technique for order of preference by similarity to ideal solution (TOPSIS), based on the calculation of each distance between each alternative and specific point.

Table 11 shows the rank of different alternatives. For CRITIC, TOPSIS, VIKOR, and SAW, the following was used as the weighting vector: ω = {0.20, 0.18, 0.17, 0.15, 0.13, 0.10, 0.07}, using a coefficient of 0.90 for the Manhattan distance and 0.10 for infinite one in the VIKOR method. As shown, the proposed extensions (IPOWA-S-CRITIC, IPOWA-W-CRITIC, IPOWA-S-W-CRITIC) show a very high correlation, obtained from Spearman's correlation coefficient (r_s), with respect to the IPOWA CRITIC method, being higher than 0.64 in all cases, and also of the IPOWA CRITIC with respect to other methodologies such as CRITIC, TOPSIS, VIKOR and SAW, being higher than 0.76 in all of these cases.

The analysis of the sustainability of the agroecological strategies used in Muchamiel tomato cultivation, obtained from the IPOWA-CRITIC ranking, offers very consistent results. To improve the interpretation of the results, Figure 2 shows the average allocation values obtained for each of the main factors used. The sustainability of the agronomic strategies is inversely proportional to the values obtained.

The Muchamiel tomato is a crop that is well adapted to Mediterranean edaphoclimatic conditions (Garcia-Martínez, 2016). Thus, the values of commercial production in the treatments without shading are adequate. The plants with fixed and conventional shading had lower production values, perhaps due to reduced photoassimilates. However, their nutritional composition was the best in all the treatments applied. This effect coincides with what was described by Milenkovic et al. (2020); in our experimental conditions, the shading nets were not beneficial for Muchamiel tomatoes. Despite the tomato plants using diffused light more efficiently than direct radiation (Hemming et al., 2008), in our experimental conditions, photoassimilation was limited, negatively affecting production and the index of maturity. It is possible that the reduction in solar radiation caused by the shading nets used was excessive. Milenkovic et al. (2020) used shading nets of different colors, with a reduction in solar radiation similar to that recorded in our study, with tomatoes Optima' F1 and “Big beef” F1, finding an improvement in production and quality. These results can be explained, considering the influence of the genetic material on the response of these plants to these types of treatments. In addition, the color nets have an influence not only on the quantity of solar radiation that reaches the plants but also on their quality (Timmermans et al., 2020).

The shading treatments showed significant differences considering the sustainability criteria used. Thus, the sustainability of the Muchamiel tomato crop without shading and with mobile photovoltaic shading was similar between them, and was higher than the plants with conventional fixed shading (Figure 2). This is because the 50% fixed shade used in the assay limited the photosynthetic activity of the plants, reducing production. Among the no-shade treatments, only the non-grafted plants with full irrigation obtained an unfavorable score (treatment A₃ occupies 10^th place, Table 11). Independent of the shading used, this result can be generalized, that is, the non-grafted plants and those with full irrigation had the worst behavior (treatments A₃, A₇, and A₁₁ occupied the 10^th, 12^th, and 6^th positions, respectively, Table 11). The results can be explained if we take into account the effect of the graft on production. Non-grafted plants had a lower production, perhaps due to the presence of fungi and nematodes in the soil, which reduce the absorption of water and nutrients (Phani et al., 2024). Intensive production to maximize performance and satisfy demand makes attacks by plagues and diseases critical threats for producers, in both field conditions and greenhouses (Capinera, 2020; Phani et al., 2021). Despite the heavy losses due to the action of nematodes, the management options of this disease are limited, highlighting grafting among them (Martínez-Ballesta et al., 2010). Therefore, the full irrigation of non-grafted plants implies higher operational costs, although the production is lower than that of the treatments that used grafted plants, so the use of non-grafted plants is particularly unfavorable in our experimental conditions.

The use of grafts tends to improve the sustainability of the production, considering the criteria utilized (Figure 2). Thus, the evaluation of the grafted plants was better than that of non-grafted ones, in every case, with the exception of the plants under photovoltaic shading and deficit irrigation treatment (treatments A₁₀ and A₁₂, Table 11). This exception is particularly notable, as treatment A₁₂ (PND) was found in first place (Table 11). This result can be explained by considering the multicriteria analysis performed. In this way, the use of deficit irrigation, along with the mobile photovoltaic mesh, mitigates the reduction of production, which implies the use of non-grafted plants in the experimental conditions utilized. On the one hand, the appropriate deficit irrigation of tomatoes can save a significant amount of water and improve quality, without negatively affecting production or the economic results (Khapte et al., 2019). On the other hand, the shade produced by the photovoltaic installation can avoid the reduction in tomato production (Ureña-Sánchez et al., 2012). This could be true, especially in our case, due to the use of the mobile mesh, which only produces shade in the middle of the day. In this way, the photoinhibition of tomato could be avoided, which is produced by excessive radiation, and can lead to a reduction in photosynthetic activity and production (Shi et al., 2022; Wang et al., 2018).

Tomato crops demand a large amount of water (Peet, 2005), especially during the flowering phase (Khapte et al., 2019). Deficit irrigation in the experimental conditions utilized improved the sustainability of the Muchamiel tomato. Thus, the top four treatments used deficit irrigation, and in general, all the even-numbered treatments (deficit irrigation) were better evaluated, that is, their allocation is lower than the odd-numbered treatments just before (identical treatment with full irrigation) (Table 11). Irrigation is a determining factor in the sustainability of production. In areas with warm and/or Mediterranean climates, with a scarcity of water, the maximization of the productivity of water of crops can be more beneficial for the farmer than the maximization of crop performance (Pereira et al., 2002).

Currently, the Overall Life Cycle Sustainability Assessment (OLCSA) is the most utilized method to estimate the sustainability of products, goods, or services. The application to agri-food systems has specific limitations related to the definition of the system, the interval of assessment, or the spatiotemporal resolution of the databases utilized, among others. In addition, there is a potentially high variability between independent agricultural businesses due to the differences on cultivation practices, agroclimatic conditions, seasonality, and distances between the places where activities considered in the lifecycle of the product are carried out (Notarnicola et al., 2017). To avoid these inconveniences, the application of multicriteria methods has been recently developed, such as the one proposed in the present study, which seeks to organize the different strategies as a function of the environmental, economic, social, cultural, etc., criteria. Thus, studies have been conducted on the valorization of agricultural waste (Escalante et al., 2016), joint management of agro-livestock farms and agricultural farms (Reyna-Ramírez et al., 2025), tomato ketchup packaging design (Wohnera et al., 2020), irrigation management in conditions of water deficit (Montazar and Snyder, 2012), fertigation management in tomato cultivation (Heiba et al., 2023), strategies for improving field-grown cereal yield (Di Bene et al., 2022), and evaluation of the sustainability of tomato cultivation (Sadiq et al., 2025). Overall, this is a very interesting approach and is considered a suitable complement to OLCSA methods for decision-making in the agri-food value chain. The continued performance of this type of analysis is needed to make advances on the sustainability of the agricultural sector, improve its resilience, and respond to societal demands.