Agricultural Systems 216 (2024) 103909 0308-521X/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by- nc/4.0/). A biophysical suitability model to identify best areas for the cultivation of potential cash crops: The case of basil in Valle del Cauca Maria del Mar Esponda-Bernal a,*, Andrés Fernando Echeverri-Sanchez b, Eduar Fernando Aguirre-Gonzalez c, Robert Santiago Andrade a a PISA4 Impact-FAE, International Center for Tropical Agriculture (CIAT), kilometer 17 Cali-Palmira, Palmira, Colombia b School of Natural Resources and Environment Engineering (EIDENAR), Universidad del Valle, 13th Street #100-00, Cali, Colombia c Industrial Engineering Program, Universidad del Valle, 13th Street #100-00, Cali, Colombia H I G H L I G H T S • We identify and prioritize the key biophysical factors for optimal basil development in Valle del Cauca, Colombia • Southern Valle del Cauca, notably Cali and Jamundí, offers 161,052 ha of prime land for basil production. • Confirm the department’s suitability and potential for basil cultivation using our model. • We provide essential insights to decision-makers, producers, and organizations for effectively promoting this crop. A R T I C L E I N F O Editor: Leonard Rusinamhodzi Keywords: Basil Suitability model Multicriteria analysis Biophysical Fuzzy functions Valle del Cauca A B S T R A C T CONTEXT: Basil (Ocimum basilicum L.) cultivation in Colombia has increased exponentially since 2006 (over 41- fold in relation to 2020), driven by a favorable export market. It is a particularly promising crop for creating livelihood opportunities in the region, especially for vulnerable populations such as victims of forced displace- ment and single mothers. Basil cultivation can foster economic empowerment, strengthen the community social fabric, and support sustainable development. Based on previous studies, Valle del Cauca has suitable soil and climatic conditions for this crop, making it one of the country’s most promising regions for its cultivation. OBJECTIVE: Our study aimed to create a model to identify biophysically suitable zones in Valle del Cauca for basil cultivation. METHODS: We used a variety of techniques for our model. First, we conducted a comprehensive literature review to identify the criteria for the biophysical suitability model. We then used a multi-criteria analysis methodology to evaluate the weight of each criterion, indicating its relative importance for the crop. We subsequently applied the Suitable Crop Location Index (SCLI) using spatial analysis techniques to generate a suitability map, illus- trating the most suitable areas for basil cultivation. Lastly, we conducted a sensitivity analysis to identify the critical factors and the model’s stability. We used Geographic Information Systems (GIS) tools that integrated fuzzy suitability functions and aptitude criteria weighting, drawing on the Analytical Hierarchical Process. Our model explored a primary and a secondary scenario to assess the suitable areas in the event of average and 75% rainfall exceedance. RESULTS AND CONCLUSIONS: Our study model identified the southern part of the department as the most suitable for basil production, particularly the municipalities of Cali and Jamundí. These areas, currently used primarily for sugarcane cultivation, offer 161,052 ha of suitable land (categorized as good and very good), ac- counting for 5% of the territory studied. SIGNIFICANCE: While the model could be further refined by considering the socioeconomic and ecosystem in- formation, this study provides valuable information for decision-makers, producer associations, and organiza- tions interested in promoting, investing in, and establishing basil production as a commercially viable crop, by facilitating the identification of the most suitable cultivation areas for its production. * Corresponding author. E-mail address: m.esponda@cgiar.org (M.M. Esponda-Bernal). Contents lists available at ScienceDirect Agricultural Systems journal homepage: www.elsevier.com/locate/agsy https://doi.org/10.1016/j.agsy.2024.103909 Received 21 July 2023; Received in revised form 4 January 2024; Accepted 27 February 2024 mailto:m.esponda@cgiar.org www.sciencedirect.com/science/journal/0308521X https://www.elsevier.com/locate/agsy https://doi.org/10.1016/j.agsy.2024.103909 https://doi.org/10.1016/j.agsy.2024.103909 https://doi.org/10.1016/j.agsy.2024.103909 http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/ Agricultural Systems 216 (2024) 103909 2 1. Introduction Colombia has seen a significant increase in the production of me- dicinal and aromatic plants (MAPs) since 2006, from 767 tons in 2006 to 31,824 tons in 2020 (MADR, 2019, 2022), This represents a 41.5-fold increment since 2006, which is both substantial and significant, for instance, when compared to cereal crops.1 Among the MAPs, basil, chive, mint, laurel, and oregano have experienced the highest growth rates, and are in high demand on international markets (Vega, 2018). Basil (Ocimum basilicum L.) production has particularly increased, from 56 tons in 2008 to 4097 tons in 2020, with an increase in the planted hectares from 26 ha in 2008 to 543 ha in 2020, and higher average yields (from 1.8 t/ha in 2008 to 6.5 t/ha in 2020), as reported by (MADR, 2019, 2022). Basil is in high demand in the export market (Semana, 2023; Vega, 2018), especially in the United States (Legiscomex, 2023; Pinillos Angarita, 2016). In recent years, several studies have highlighted the profitability of MAP cultivation for export, particularly with regard to fresh basil export to the United States, highlighting Colombia’s potential in this market (Acevedo-Durán, 2019; Cajamarca-Larrota et al., 2018; Poveda-Trespalacios and Pinzón, 2019; Rey-Sepúlveda, 2014; Santos- Orduz and Manrique-Ruíz, 2020). Specifically for basil, a 2020 study by Moreno-Reyes and Silva (2020) demonstrates a positive financial indicator. Small producer associations have established basil cultivation for export, primarily cultivated by women heads of households, victims of forced displacement, and young people in the area (Bonilla, 2022; Gobernación del Huila, 2022; Piñeros-Martinez, 2022; Yáñez-Vargas, 2022). Thus, basil cultivation presents an economic opportunity for vulnerable communities and can potentially strengthen the social fabric in Colombia (Muñoz et al., 2021). Valle del Cauca is one of the de- partments with the greatest potential in Colombia for basil production (CCI, 2007; Saldarriaga-Correa, 2014; National Agricultural Survey 2006, as cited in Aldana, 2015), where it has favorable soil and climatic conditions for its production, and where basil is found sub- spontaneously between the municipalities of Palmira and Cerrito (Gar- cía-Barriga, 1975). The Colombian government recognizes the potential of aromatic herbs and has developed research projects and strategies to promote their production and commercialization (CORPOICA et al., 2016). However, more needs to be done to assess the suitability of the different regions for aromatic herb production and to support small producers. Land suitability analysis can guide the establishment of new projects, ensuring that resources are allocated effectively and that the cultivation of aromatic herbs can thrive. Suitability techniques analyze the inter- action between location, development actions, and environmental fac- tors to classify their suitability for a particular use (Malczewski, 2004). The Analytic Hierarchy Process (AHP) is a well-established and widely-used multi-criteria decision-making method that has proven instrumental in weighing and prioritizing diverse criteria when assess- ing land suitability for agricultural purposes (Akinci et al., 2013; Malczewski and Rinner, 2015; Shaloo et al., 2022). AHP has been found to be the most suitable process for handling multi-criteria data that are heterogeneous in nature (Chivasa et al., 2022). This methodology aligns with the integrated Multi-Criteria Evaluation (MCE) approach and geospatial techniques, and holds great potential for improving result accuracy (Chivasa et al., 2022; Ramamurthy et al., 2020) and addressing the crucial need to maximize food production from existing cultivable land (Seyedmohammadi et al., 2019). This study has selected the AHP-GIS (geographical Information Systems) methodology to identify suitable areas for basil cultivation in Valle del Cauca, Colombia, in order to optimize decision-making and promoting informed agricultural development. The selection of this department allows to take advantage of elements that favor develop- ment which are not implicit in the model, such as its good infrastructure, access to specialized inputs and services, the active presence of in- stitutions that promote agricultural development, as well as its strategic location with access to nearby seaports and airports, offering trade and export opportunities towards national and international markets. Beyond the work carried out by Rural Agricultural Planning Unit (UPRA) at the national level, there are limited studies applying this methodology in Colombia (Anacona Mopan et al., 2023; Córdoba- Colombia et al., 2018; Rivera., 2023), particularly in the context of non- traditional crops. Therefore, this study is the first to apply this meth- odology to address the topic of aromatic plant cultivation in Colombia. 2. Materials and methods To identify suitable areas for basil cultivation, we used a variety of techniques (Fig. 1) in a step-wise process. First, we conducted a comprehensive literature review to identify the criteria for the bio- physical suitability model. Second, we used a multi-criteria analysis methodology to evaluate the weight of each criterion, indicating its relative importance for the crop. Third, we developed a spatial suit- ability analysis model to integrate all the information. Fourth, we applied the Suitable Crop Location Index (SCLI) to generate a suitability map, identifying the most suitable areas for basil cultivation. Lastly, we conducted a sensitivity analysis to identify the critical factors and the model’s stability. 2.1. Study area The department of Valle del Cauca (Fig. 2) is located in southwestern Colombia, between 3◦ and 5◦ N and 75◦ and 77◦ W, at an average alti- tude of 1000 m above sea level, covering an approximate area of 22,000 km2. It integrates the active continental margin of South America, at the site of interaction with the Nazca and Caribbean tectonic plates. The associated continental feature is the Andes Mountain range, which is divided into three cordilleras separated by intramontane valleys (Ser- vicio Geológico Colombiano, 2001). Here, the average temperature fluctuates between 23◦ and 24◦ Celsius, with relative humidity between 65% and 75%. Annual precip- itation indices are 1589 mm in the north (133 rainy days), 1882 mm in the south (109 rainy days), and 938 mm in the central region (100 rainy days) (IDEAM, 2015). The area has a bimodal climate pattern with a rainy season in the months of April–May and October–November (ac- counting for 70% of annual precipitation) and two dry seasons in January–February and July–August (Armbrecht and Ulloa-Chacon, 1999). Valle del Cauca has been used for agricultural and livestock activities since the 16th century, when Spanish colonization began, and then intensively in the mid-20th century for the industrial cultivation of sugarcane (Vargas, 2012). Currently, the main crops planted are sug- arcane, coffee, and bananas (MADR, 2022). 2.2. Criteria for optimal basil cultivation To determine the criteria for optimal basil cultivation, we conducted a literature review focused on basil cultivation. For this, we search on platforms such as Web of Science and Scopus, local university library catalogs and agricultural agency publications. Initially we had >30 documents, however when we reviewed them and got to the primary source, we were left with 10 documents that simultaneously have spatial information within the study area. Among our final literature we found review articles and books published by government agencies. Our re- view indicated that basil has not been widely studied. Among the 10 articles presenting relevant data, published between 2004 and 2019, we 1 For Colombia, cereal yields more than doubled from 1,664,101 to 5,548,357 tons during the same period. In this category we include oats, corn, wheat, barley, millet, quinoa, and sorghum. M.M. Esponda-Bernal et al. Agricultural Systems 216 (2024) 103909 3 identified the following biophysical criteria: altitude, drainage, sun- shine, relative humidity, precipitation and temperature (Table 1). Slope gradient was included as it affects the distribution of daytime sunlight, which is essential for optimal basil growth. Exclusion criteria included urban areas, water bodies, protected areas, and lands with advanced erosion. While soil characteristics play an important synergistic role in cultivation, we did not include more specific soil criteria such as pH, salinity, organic matter content, taxonomy or texture, because this in- formation was either not available for basil or has not been sufficiently studied in the study zone to create the layer to be included in the model. 2.3. Criteria normalization To integrate the absolute values of the different variables for the selected criteria, a common scale is needed. To avoid biased results, normalization is required. These processes scale all criteria down to the same level while maintaining the relative importance of each criterion. Membership functions are used for this purpose (Bellman and Zadeh, 1970; Bonham, 1994). These functions range between 0 and 1, indi- cating the degree to which an element belongs to a set. All these func- tions can exhibit increasing, decreasing, or symmetric graphical trends and are defined by parameters or control points that correspond to the extreme and optimal values of the variables (Dubois and Prade, 1980; Duprey and Taheri, 2009; Gill and Bector, 1997). To evaluate and normalize the factors affecting basil growth, we used a variety of fuzzy functions. These functions were selected to best reflect the criteria described in the consulted literature (Table 1). We used a scale ranging from 0 to 1 to normalize the values, where 1 represents the most favorable conditions for basil growth. For example, a value of 0.7 for temperature indicates that the temperature is slightly below the ideal range. We used composite functions for both precipitation and temperature factors. The parameters of the fuzzy functions are provided in Table 2 of the supplementary material. Figures Fig. 3 and Fig. 4 depict the fuzzy functions used for each criteria. The drainage factor was evaluated based on a qualitative scale (Table 2 supplementary material), which was transformed into a quantitative scale from 0 to 18, where the lowest values corresponded to very high levels of drainage and the highest values to very poorly drained soils. Of the criteria considered for exclusion, water bodies and urban areas were assigned a value of zero, while land erosion was evaluated based on different erosion degrees (see Table 2). Regarding the pro- tected areas, only Civil Society Nature Reserves were not excluded from the model (Decree 1996 of 1999, 1999). 2.4. AHP approach The Analytical Hierarchy Process (AHP) is a multi-criteria decision- making process that uses analytical hierarchies to determine the importance of criteria and their associated relationships in complex problems (Brandt et al., 2017; T. L. Saaty, 1977, 1980). It supports decision-makers in selecting the best alternative based on multiple criteria and sub-criteria (R. W. Saaty, 1987). The AHP is based on three steps. Step 1: Transform the multi-criteria decision-making problem into a hierarchy model. The model has three levels: the goal at the top, the criteria in the middle, and the alternatives at the bottom. These three levels are the minimum requirement for the hierarchy model, though we can add other layers of sub-criteria between the criteria and the alternatives, if required. Step 2: Identify the importance of one criterion over another. Comparative judgements are made by constructing pairwise comparisons between criteria. A scale is proposed by R. W. Saaty (1987) (Table 3) helps to find one- to-one correspondence between the set of alternatives and a subset of rational numbers, which represent the importance of of ith alternative Fig. 1. Framework for the biophysical suitability model for basil cultivation. AHP (Analytical Hierarchical Process); SCLI (Suitable Crop Location Index). M.M. Esponda-Bernal et al. Agricultural Systems 216 (2024) 103909 4 over the jth alternative. Suppose we have n alternatives to be compared pairwise. If aij denotes the preference of ith alternative over the jth alternative, where i, j = 1, 2, …, n. Such pairwise comparisons are used to find the importance of one alternative over another in terms of each criterion. Then these relative preferences form a positive reciprocal matrix A = [aij] of order n, where aii = 1 ∀i = 1, 2, …, n and aij = 1 aji, i, j = 1, 2, …, n. An n ×n pairwise comparison matrix of order n can be rep- resented as follows: A = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ a11 a12 … a1n a21 ⋮ an1 a22 ⋮ an2 … ⋱ … a2n ⋮ ann ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ a11 a12 … a1n 1 a12 ⋮ 1 a1n a22 ⋮ 1 a2n … ⋱ … a2n ⋮ ann ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ (1) (3) Step 3: Perform the priority vector (weights) and consistency of the judgements. Consistency Ratio (CR) shows the likelihood that the ratings were developed by chance. The ideal CR is zero (0). However, in practice achieving zero is difficult. To be accepted the CR must be <10%, and if CR > 10% then the decision maker should re-evaluate the pair-wise comparison to identify the source of inconsistency and resolve it and repeat the analysis until CR reaches an acceptable level (R. W. Saaty, 1987). To gather expert opinions to hierarchize the selected biophysical criteria related to the development of basil cultivation, we developed an online questionnaire, 2 which utilized comparative judgements through the Saaty rating scale (see Table 3). We initially took a database of 198 companies that were somehow involved in working with aromatic plant cultivation in Colombia. After that, we filtered out those companies that had no connection to basil cultivation, resulting in 16 companies. Next, we contacted these 16 companies by phone, specifically reaching out to the key individuals responsible for crop management, and received re- sponses from 11 of them. We also contacted two expert basil researchers who have experience in its production, bringing the total number of respondents to 13. The questionnaire was completed between August and October 2020, each providing individual aggregate ratings.3 After collecting the ratings from each expert, we used the AHP methodology to process the data and determine the weighting of each factor (Table 4). One expert (a producer) was excluded from the final weighting process due to the respondent’s Consistency Radius (CR) value being too high (a value of >0.1). The remaining 12 experts’ responses were used to calculate the final weighting of the factors, resulting in a final general CR of 0.019. 2.5. Generation of land suitability map using GIS Data for precipitation, temperature, relative humidity, and sunshine Fig. 2. Department of Valle del Cauca. 2 Survey of Basil Crop Criteria Evaluation (2020).Survey available at http s://www.questionpro.com/t/AQ1F0ZiKej 3 Results of the survey are available in https://cgspace.cgiar.org/handle/105 68/130936 M.M. Esponda-Bernal et al. https://www.questionpro.com/t/AQ1F0ZiKej https://www.questionpro.com/t/AQ1F0ZiKej https://cgspace.cgiar.org/handle/10568/130936 https://cgspace.cgiar.org/handle/10568/130936 Agricultural Systems 216 (2024) 103909 5 were collected from meteorological stations within the study area. Monthly multiannual data series between 1981 and 2010 were selected and cleaned to ensure that each series had a minimum of 10 years of continuous data. Spatial and alphanumeric information was obtained from regional and national institutions’ published data (see Table 5). To build the necessary modeling layers, the spatial data was converted into a 100-m resolution raster format using ArcGIS 10.6 software. Precipitation, temperature, relative humidity, and sunshine criteria were interpolated from the alphanumerical information provided by the stations. Before interpolation, box plots and time series graphs were used to identify potential errors or outliers. The normality of all series was tested using the Kolmogorov-Smirnov test, and the semivariogram parameters were estimated using Gamma Design software. Kriging was used to interpolate the precipitation series, which had an R-squared value above 70%, while Inverse Distance Weighting (IDW) was used to interpolate the other criteria. The model evaluated two scenarios: average precipitation, and a Weibull4 precipitation5 exceedance prob- ability analysis for 75% (Weibull, 1951). The exceedance scenario was used to assess crop suitability in case of reduced precipitation. The factors were integrated using a weighted sum of the previously normalized raster layers and the weights assigned to each criterion (Table 4). The exclusion criteria layers were then normalized by multiplying directly with the raster generated in the previous weighted sum. The weighted sum tool and the raster calculator tool — both included in the ArcGIS software spatial analyst package — were used to perform weighted sums and multiplications. This process was repeated for each precipitation scenario (average and 75% exceedance). The SCLI was classified into six categories (Table 6). The FAO clas- sification (FAO, 1976) (S1, S2, S3, N1, N2) is widely used and recog- nized in the scientific community, but we focused on providing a more detailed suitability classification to obtain more granular results. 2.6. Sensitivity analysis The Sobol’ variance-based method was proposed for the sensitivity analysis as it is widely applied in the field of numerical modeling (Saltelli et al., 2010) and spatial models (Lilburne and Tarantola, 2009; Xu et al., 2020; Zajac et al., 2015). The method decomposes the variance of the model output and obtains measures of sensitivity for both indi- vidual model inputs (factors and weights) and combinations of inputs (Saltelli et al., 1999). It is very appropriate for complex geographical models because such models are rarely additive and linear, and is therefore not sufficient to explore the inputs individually. Instead, the inputs must also be explored in combination with an increasing level of dimensionality (Peñacoba-Antona et al., 2021). To assess the Sobol’ variance-based method we employed the SimLab (Joint Research Centre, 2010). We initially examined the frequency distribution of the factors, which was calculated based on histograms generated from each normalized raster. Subsequently, for the weights (as presented in Table 4), we assigned a uniform distribution with a ± 20% variation from their nominal values. We then conducted Monte Carlo analysis, sampling both the factors themselves and their corre- sponding weights, resulting in a total sample size of 1920 values. Additionally, Sobol’ method was applied a total of 17,280 times in our analysis. First-order indices measure the average influence of a factor on the model output. A higher index indicates a greater effect of the factor on the model. Total indices add up all the factor indices, including the first- order effect and any additional effects that arise from interactions among factors. This calculation provides a complete picture of the fac- tors overall influence on the model (Monserrat and Barredo, 2006). Table 1 Summary of suitability data available for basil cultivation drawn from our study’s literature review. Criteria Description Reference Altitude The altitudinal range for basil cultivation can be 0–2600 m.a.s. l. (under greenhouse conditions), but when grown outdoors (not under greenhouse conditions), it adapts well at 0–1600 m.a.s.l. Alarcón Restrepo (2011) Drainage1 Basil requires well-drained soils since it cannot tolerate waterlogging. (AMARANTO, 2015; Bonilla- Correa et al., 2011; CCI, 2007; Gobernación de Antioquia, 2014) Sunshine2 To ensure optimal basil growth, the plants require 16 h of sunshine under greenhouse conditions. Cortés and Clavijo (2008) In cases of free exposure, the species grows better in long-day conditions, which means that more hours of sun exposure are preferable. (Makri and Kintzios, 2008) Relative Humidity The optimum relative humidity for basil growth ranges from 60% to 70%. CCI (2007) Precipitation To ensure proper development of basil, it is estimated that 300–400 mm of water spread over the growing season is required. For a basil crop in free exposure, there are 4.8 cycles per year (Bonilla-Correa and Guerrero-Rojas, 2010), which means that rainfall of 1440–1920 mm per year is necessary. INIA (2004) A requirement of 1500–2000 mm per year. Gobernación de Antioquia (2014) Temperature The optimum temperature range for basil growth is 24–30 ◦C during the day and 16–20 ◦C at night. Cortés and Clavijo (2008) In general, increasing air temperature to 29 ◦C resulted in an increase in fresh and dry weight accumulation, node number, percent of plants with visible flower buds or flowers, plant height, internode length, branch number, and chlorophyll fluorescence. Walters and Currey (2019) The optimum temperature was found to be 28 ◦C for the relative growth rate. Caliskan et al. (2009) Slope The optimal slope gradient for aromatic plants, in general, should be <12%. Gobernación de Antioquia (2014) Note: The effect of temperature on the growth of four basil varieties is illustrated in the supplementary material. 1 Drainage is the removal of excess water and dissolved salts from the surface and subsurface of the land in order to enhance crop growth (FAO, 1996). 2 Sunshine is the measurement of the hours of effective sunshine in a day (solar brightness or insolation), which is associated with the amount of time during which the ground surface is irradiated by direct solar radiation (IDEAM, 2017). 4 The Weibull distribution was selected because of its simplicity, versatility, and ability to approximate exponential, normal, and/or skewed distributions (Barili et al., 2022; Rahmani et al., 2014; Reeve, 1996; Schönwiese et al., 2003; Weibull, 1951; Wilson and Toumi, 2005). Moreover, Weibull distributions are commonly used to analyze climate data, especially when studying the likeli- hood of extreme precipitation events (Kotz and Nadarajah, 2000; Olivera and Heard, 2019; Rahmani et al., 2014). 5 Only the precipitation criterion was considered for the scenario, as it has the highest number of meteorological stations and, therefore, is the most robust climate criteria. M.M. Esponda-Bernal et al. Agricultural Systems 216 (2024) 103909 6 2.7. Validation In addition to the model results, empirically-derived quantitative information is needed to validate the classification (Rossiter, 1990; Van Lanen and Bouma, 1989). When validating a model, the aim is to ensure the accuracy and reliability of its predictions, providing insights into its quality and performance. In summary, model validation is crucial to ensure its reliability, good generalization to new data, and effectiveness in real-world situations, thereby establishing a solid foundation for decision-making and implementation in various contexts. Ideally, the crop yield response should be empirically evaluated from multi-environmental trials conducted in each land suitability class, as recommended by Huajun and Van Ranst (1992). In our specific case, we Fig. 3. Fuzzy functions of climatic factors. 1: Sunshine, 2: Precipitation, 3: Relative Humidity, and 4: Temperature. Fig. 4. Fuzzy functions of physical factors. 1: Altitude, 2: Drainage, and 3: Slope gradient. Table 2 Standardization of erosion criteria. Erosion degree1 Normalized value Natural 1 No evidence 1 Slight 0.75 Moderate 0.50 Severe 0 Very severe 0 1 Soil erosion is broadly defined as the accelerated removal of topsoil from the land surface through water, wind or tillage (FAO, 1996, p. 100). M.M. Esponda-Bernal et al. Agricultural Systems 216 (2024) 103909 7 have access to municipal-level yield data from the National Agricultural Survey (MADR, 2022) for the year 2021 within the category of “Aro- matic and Medicinal Plants”. Based on this we could calculate the cor- relation between the yield of aromatic plants and the average value of the SCLI index, both at the municipal level. We conducted these calcu- lations using RStudio. 3. Results and discussion 3.1. Criteria normalization The suitability level determined using fuzzy logic functions is normalized from zero to one, with zero indicating no suitability and one representing maximum suitability. After normalizing the factors, we can identify the most restrictive ones for crop suitability, as shown in Fig. 5. In exclusion areas6 only, the suitable area decreases to 789,632 ha, representing 38% of the study area. On the other hand, the most restrictive factors are represented by areas with extreme values of pre- cipitation and steep slopes. In the precipitation scenario with 75% ex- ceedance, there is a reduction in the suitable area, especially in the geographical valley.7 Finally, it would be beneficial to incorporate a global erosion layer, taking into account the geographical valley. 3.2. Aptitude categories Upon integrating the factors, we can determine the most suitable areas for basil cultivation under different rainfall scenarios. We classi- fied the homogeneous suitability areas into six categories: very good, good, regular, low, very low, and exclusion, the latter with zero suitability. Fig. 6 shows the suitability map for basil cultivation under average Table 3 Pairwise comparison matrix used in the study’s online questionnaire. Source: Taken from (R. W. Saaty, 1987). Intensity of importance on an absolute scale 1/9 1/7 1/5 1/3 1 3 5 7 9 Extreme Very strong Strong Moderate Equal Moderate Strong Very strong Extreme Less important Equal importance More important Table 4 Weighting factors according to AHP methodology calculated on experts’ re- sponses to an online questionnaire through the AHP methodology. Criteria Hierarchy Weighting [%] Sunshine 1 19.1 Temperature 2 19.0 Relative humidity 3 14.7 Altitude 4 12.7 Precipitation 5 12.6 Drainage 6 12.3 Slope 7 9.5 Table 5 Input information for factors within the model. Criteria Description Format Reference Altitude Digital Elevation Model with a 30 m resolution in m.a.s.l. Raster (CVC, 2015) Slope1 Sunshine2 Monthly average daily sunshine hours from 18 climatological stations. Alphanumeric (CENICAÑA, 2020; CVC, 2020; IDEAM, 2020) Relative Humidity2 Average monthly relative humidity percentage (dimensionless) from 24 climatological stations. Precipitation2 Multi-year monthly total precipitation in millimeters from 652 stations (climatological, evaporimetric, pluviographic, and pluviometric). Temperature2 Multi-year monthly average temperature values in degrees Celsius from 24 climatological stations. Drainage Derived from the land-use capability map of Valle del Cauca. It includes 18 qualitative categories related to drainage (see supplementary material). Vector (GEOCVC, 2019; IGAC, 2006) Urban areas Derived from the land capability map of Valle del Cauca. It includes the urban areas. (IGAC, 2006) Erosion Contains the qualitative erosion levels for the department (Table 2) (GEOCVC, 2003) Water bodies Contains the main bodies of water in the department (GEOCVC, 2020) Protected areas Contains the boundaries of natural parks and other protected areas in the department (PNNC, 2014) 1 The Slope tool from the ‘Spatial Analyst’ package of ArcGIS software was executed based on the Digital Elevation Model (expressing the result in percentage). 2 Data were collected for the years 1981–2010. Table 6 Six suitability categories used for the Suitable Crop Location Index (SCLI). Suitability categories SCLI Range Qualitative description Exclusion 0 Areas where crop establishment is not feasible due to severe limitations. Very low 0–20 Areas presenting severe limitations that prevent the sustainable and profitable use of the land for cropping. Low 20–40 Areas presenting high limitations that prevent the sustainable and profitable use of the land for cropping. Regular 40–60 Areas presenting moderate limitations that reduce productivity and limit the correct development of the crop. Good 60–80 Suitable areas for crop establishment, but with minor limitations that may reduce optimal productivity. Very good 80–100 Optimal areas for crop establishment. 6 Exclusion areas are defined as areas that lack suitability based on specific criteria, including urban areas, waterbodies, protected areas, and land with advanced erosion. 7 The geographical valley of the Cauca Valley is an extensive plain that ex- tends along the course of the Cauca River in Colombia. This valley is charac- terized by its relatively flat and fertile topography, flanked by mountain ranges on both sides. M.M. Esponda-Bernal et al. Agricultural Systems 216 (2024) 103909 8 precipitation conditions, while Fig. 7 shows the categories under the Weibull 75% exceedance. The most suitable areas are located in the southern part of the geographic valley, where slopes are gentle and erosion is low. The main limitations are represented by high precipita- tion and low sunshine in the Pacific zone, as well as steep slopes in the western and central mountain ranges. Table 7 provides information on the suitability ranges and the area (in hectares) in each category. Most of the study area is concentrated in the exclusion (which applies to any crop), low, and regular aptitude categories. Nevertheless, there are a considerable number of hectares (161,052 ha) categorized as good and very good. It worth highlighting that, under the Weibull 75% exceedance precipitation scenario, the best aptitude areas are reduced almost to the point of disappearing (Fig. 7 which indicates basil’s high sensitivity to rainfall. Specifically, it reduced the very good classification area of the department from 0.3% to 0.1%. Another significant change under the 75% exceedance scenario is that the good zones in the center of the geographical valley changed to the regular category, and the highest suitability area categorized as very good changed to good, i.e., in the southwest zone of the municipality of Cali. 3.3. High-aptitude zones We classify as “high-aptitude” the zones falling within the very good and good aptitude categories. Examining the distribution of suitability categories across different regions reveals that the majority of suitable areas in both scenarios are located in the geographical valley (Table 8, Table 9). Specifically, we identified 29 municipalities within the good category, totaling 118,618 ha and 96,287 ha under average precipita- tion and 75% exceedance scenarios (see supplementary material). Regarding the very good category, this number is reduced to 5104 ha and Fig. 5. Normalized criteria. M.M. Esponda-Bernal et al. Agricultural Systems 216 (2024) 103909 9 2270 ha for average precipitation and 75% exceedance scenarios, respectively, focusing on the municipalities of Cali and Jamundí (Table 10). It is worth noting that, under the second scenario (decreased rainfall), many areas in Cali and Jamundí change from the very good category to the good category, while the 78 ha identified in Candelaria as very good are no longer included in the high-aptitude zone altogether (Table 10). Using the average SCLI, a more detailed analysis of the optimal locations at the village level identified the villages of La Viga, Peón, El Banqueo, and Valle del Lili as those with the highest potential. These villages keep their potential even under the second (decreased precipitation) scenario. Currently, the best-suited areas for basil production are primarily used to cultivate sugarcane, as shown in Table 11. Sugarcane covers between 64% to 99% of the agricultural land in these municipalities, which is a significant portion. The region’s favorable growing conditions and well-established infrastructure in the geographical valley make it an ideal location for growing crops such as basil. Suitable locations for basil cultivation in Valle del Cauca benefit from several facilities, including a wide coverage of the road network. Spe- cifically, the National Route 25 (Western Trunk Road) runs through the most suitable areas, spanning 0–13.6 km (Instituto Nacional de Vias, 2022), thereby encompassing the zones of interest. Furthermore, these locations are conveniently situated 16–44.6 km in a straight line from the primary airport of the department (Alfonso Bonilla Aragón) (Agencia Nacional de Infraestructura, 2021), facilitating the streamlined expor- tation of fresh produce (Barreño-Rojas, 2004). Basil cultivation could provide social benefits. While sugarcane cultivation has historically been an important source of income for farmers in Valle del Cauca and plays a significant role in the local economy (Bermúdez Escobar, 2017), this industry has also faced chal- lenges related to land disputes, labor violations, and displacement of local communities (Vélez-Torres et al., 2019). Additionally, it continues to drive significant environmental impacts, particularly if not managed sustainably, including soil degradation, reduced availability and quality Fig. 6. Homogeneous zones (average rainfall scenario). M.M. Esponda-Bernal et al. Agricultural Systems 216 (2024) 103909 10 of water and soil (Pérez et al., 2011), and deforestation and drying of wetlands for sugarcane monoculture (Vélez-Torres et al., 2019). In contrast to sugarcane, basil cultivation is typically grown on a smaller scale and presents opportunities for smallholder farmers to diversify their income sources and enhance their livelihoods (Muñoz et al., 2021). Furthermore, basil cultivation proves to be more profitable on the export market, particularly for organic production (Gobernación del Huila, 2022; Reyes-Pinilla, 2011; Santos-Orduz and Manrique-Ruíz, 2020), which is expected to result in fewer adverse environmental im- pacts than conventional sugarcane production. Other essential criteria to be considered for successful basil cultiva- tion include irrigation systems and water quality, particularly to meet export market requirements related to quality assurance. Basil requires regular irrigation every two to three days and precipitation alone is often unreliable (Makri and Kintzios, 2008; Caliskan et al., 2017; Daza- Torres et al., 2017; Naderianfar et al., 2017). Good-quality water for irrigation to avoid the risk of the irrigation system clogging (Barreño- Fig. 7. Homogeneous suitability zones for basil cultivation under a Weibull 75% exceedance scenario. Table 7 Suitability area by aptitude category. Suitability categories SCLI Range Average precipitation scenario 75% exceedance precipitation scenario Area [ha] Department area [%] Area [ha] Department area [%] Exclusion 0 789,632 38.0 789,632 38.0 Very low 0–20 152,859 7.3 167,169 8.0 Low 20–40 660,989 31.8 657,829 31.6 Regular 40–60 315,374 15.2 360,055 17.3 Good 60–80 155,313 7.5 102,888 4.9 Very good 80–100 5739 0.3 2333 0.1 SCLI (Suitable Crop Location Index). M.M. Esponda-Bernal et al. Agricultural Systems 216 (2024) 103909 11 Rojas, 2004). Since most basil is exported in fresh leaf form (Acevedo-Durán, 2019; Santos-Orduz and Manrique-Ruíz, 2020), rigorous post-harvest pro- cesses are essential to ensure its quality, for which a reliable power supply is necessary (Bonilla-Correa and Guerrero-Rojas, 2010; Santos- Orduz and Manrique-Ruíz, 2020). Phytosanitary quality is another critical criterion for organic basil production. A risk analysis based on the history of the plot should be developed to design a suitable preventive strategy for latent pests and diseases (Bonilla-Correa and Guerrero-Rojas, 2010). Although most biophysical criteria for basil cultivation have been included in the model, others, such as pH and salinity, could not be added due to a lack of data in the study zone; thus, these missing criteria should be evaluated through soil analysis before crop establishment. Lastly, even though the erosion factor has been added to the model, it would be appropriate to enter a full-coverage erosion layer into the model, which includes a study of the geographical valley. 3.4. Sensitivity analysis The first-order sensitivity index (Si) measures the average impact of a factor on the model output, without considering its interaction effects. The higher the index, the greater the influence the factor has on the model. The total-effect index (Sti) calculates the sum of the factorial indices involving the factor, including its first-order effect and all higher-order effects resulting from interaction among factors (Monserrat and Barredo, 2006). Table 12 shows that temperature, precipitation, slope, and altitude are the most sensitive factors of the model. It is not surprising that temperature emerged as the most sensitive factor in the model, given its highest weighting among the criteria. Nevertheless, it is noteworthy that temperature exhibits limited variability within the study area. Comparing Si and Sti for each parameter of the model enables us to understand the difference in the impact of each factor individually and in combination with others. Typically, Sti is greater than or equal to Si. The difference between the indices reflects the extent to which a factor interacts with other factors. In this case, the difference between the two indices is negligible, with the largest difference only 0.015. To be considered significant, the difference should be at least 0.2, as noted by Table 8 Percentage of the area by suitability category disaggregated by region for the average rainfall scenario. Zone Suitability category Exclusion Very low Low Regular Good Very good Total Geographic Valley 12.0 0.0 4.2 41.0 41.6 1.2 100 Western Range 55.1 4.1 29.7 9.6 1.3 0.2 100 Central range 41.3 14.5 35.9 7.4 0.9 0.0 100 Pacific 22.3 11.4 52.3 14.0 0.0 0.0 100 Table 9 Percentage of the area by suitability category disaggregated by region for the 75% exceedance scenario. Zone Suitability category Exclusion Very low Low Regular Good Very good Total Geographic Valley 12.0 0.0 5.6 55.6 26.6 0.2 100 Western Range 55.1 4.9 28.9 9.5 1.3 0.2 100 Central range 41.3 16.0 35.6 6.7 0.4 0.0 100 Pacific 22.3 11.4 52.3 14.0 0.0 0.0 100 Table 10 Area per municipality for the very good suitability category. Municipality Average precipitation scenario [ha] Weibull exceedance scenario for 75% [ha] Cali 3532 521 Jamundí 1494 1749 Candelaria 78 0 Table 11 Overview of the most suitable municipalities for basil cultivation. Municipality Municipality area1 [ha] Area under cultivation2 [ha] Area under sugarcane cultivation2 [ha] Area planted to aromatic plants2,3 [ha] Basil suitable area [ha] Cali 56,414 5654 4650 71.5 11,092 Jamundí 62,920 14,524 9291 4.0 23,974 Candelaria 29,578 26,191 25,972 26.0 28,927 1 Based on Colombia basic vector database (IGAC, 2022). 2 Data were obtained from the Municipal Agricultural and Livestock Evaluations (EVA) collected for the year 2020 by MADR (2022). 3 Aromatics planted include Coriander and Parsley, among others. Table 12 First- and total-effect indices for Sobol’ sensitivity analysis. Factor Average precipitation scenario Weibull exceedance scenario for 75% Si Sti Si Sti Temperature 0.2976 0.2978 0.2937 0.2964 Slope 0.2816 0.2851 0.2663 0.2701 Altitude 0.1564 0.1571 0.1753 0.1759 Precipitation 0.1245 0.1255 0.1467 0.1450 Drainage 0.0708 0.0856 0.0707 0.0857 Temperature factor weighting 0.0574 0.0576 0.0592 0.0620 Precipitation factor weighting 0.0466 0.0477 0.0378 0.0386 Altitude factor weighting 0.0332 0.0339 0.0348 0.0332 Sunshine 0.0322 0.0338 0.0287 0.0296 Sunshine factor weighting 0.0262 0.0278 0.0280 0.0286 Humidity factor weighting 0.0199 0.0284 0.0209 0.0280 Humidity 0.0148 0.0232 0.0198 0.0270 Slope factor weighting 0.0038 0.0073 0.0038 0.0075 Drainage factor weighting 0.0025 0.0173 0.0007 0.0156 Si = first-order index); Sti = total-effect index. M.M. Esponda-Bernal et al. Agricultural Systems 216 (2024) 103909 12 Monserrat and Barredo (2006). This suggests that the factors employed in the model exhibit a high degree of independence. To further explore the levels of uncertainty within the study area, conducting a more comprehensive sensitivity analysis is essential, as recommended by Arika Ligmann-Zielinska and Jankowski (2014). Given the pronounced role of temperature in our sensitivity analysis, it would be valuable to explore climate-change scenarios that incorporate tem- perature variations. 3.5. Validation Despite the constraints posed by the available data, we managed to obtain municipal-level yield data from the National Agricultural Survey (MADR, 2022) for the year 2021, specifically focusing on the “Aromatic and Medicinal Plants” category. We obtained a strong correlation (R2 = 0.73) between the yields for the year 2021 and the average SCLI by municipality. This suggests that the suitability classification produced by our model has the potential to be a valuable decision-making tool for basil production in the study area. 3.6. Future directions The next step involves taking the evaluation from the municipal to a more localized level to achieve greater precision and accuracy, evalu- ating suitable areas at a localized level to achieve higher resolution and accuracy. Also, is valuable includes socioeconomic and ecosystem as- pects to ensure economic viability of the crops with investment potential. 4. Conclusion We conducted a suitability analysis for basil cultivation in the Valle del Cauca department using a biophysical approach. Our model priori- tizes key factors affecting crop development, combining GIS and multi- criteria decision-making. We explored scenarios based on average and reduced rainfall, revealing a reduction in suitable production areas. Exclusion factors, such as slope and precipitation, ruled out mountain ranges and the Pacific zone. Our results highlight suitable areas for basil cultivation, with the most favorable located in the southwest, achieving a maximum SCLI of 86 and 88 under average rainfall and a Weibull exceedance scenario for 75%, respectively. A sensitivity analysis iden- tified temperature as the most critical factor. This exploratory study recognizes the need for further research to enhance model inputs, emphasizing its indicative nature. CRediT authorship contribution statement Maria del Mar Esponda Bernal: Writing – review & editing, Writing – original draft, Visualization, Validation, Resources, Investigation, Formal analysis, Data curation. Andrés Fernando Echeverri Sanchez: Supervision, Resources, Conceptualization. Eduar Fernando Aguirre Gonzalez: Methodology. Robert Santiago Andrade: Writing – review & editing, Supervision, Project administration. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability We have shared the link to my data at the Attach File step Acknowledgments We express our heartfelt gratitude to the following individuals and organizations for their invaluable contributions and support throughout this research project: • The REGAR research group of the Universidad del Valle for their continuous guidance, assistance, and implementation of the project. • The companies that participated in the study: Basil farm SAS, Caribbean Specialty Colombia, Spot Soluciones SAS, Ecoexport SAS, Suaga, Organic Herbs, Aromáticas medicinales del Risaralda, Agro- andina Fresh Products SAS, Jack Herbs, C&G aromatics SAS, Agrícola Villa Marcela SAS, and Eshkol Premium. Their cooperation and will- ingness to share their expertise were instrumental in gathering the necessary data and insights. • The CGIAR Initiative on Market Intelligence for their generous financial support in the publication of this study. • Olga Spellman of the Science Writing Service of the Alliance of Bioversity International and CIAT for editorial support to this manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.agsy.2024.103909. References Acevedo-Durán, A.J., 2019. Estrategias de competitividad para los productores de plantas medicinales en Colombia [Universitaria Agustiniana]. https://repositorio.uniagustin iana.edu.co/handle/123456789/927?show=full. Agencia Nacional de Infraestructura, 2021. Concesiones Aeroportuarias. 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