Land Suitability Analysis for Potato Crop in the Jucusbamba and Tincas Microwatersheds (Amazonas, NW Peru): AHP and RS–GIS Approach

agronomy

Article Land Suitability Analysis for Potato Crop in the Jucusbamba and Tincas Microwatersheds (Amazonas, NW Peru): AHP and RS–GIS Approach

Daniel Iliquín Trigoso 1,* , Rolando Salas López 1,* , Nilton B. Rojas Briceño 1 , Jhonsy O. Silva López 1 , Darwin Gómez Fernández 1 , Manuel Oliva 1, Lenin Quiñones Huatangari 2 , Renzo E. Terrones Murga 1 , Elgar Barboza Castillo 1 and Miguel Ángel Barrena Gurbillón 1

1 Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES) de la Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Chachapoyas 01001, Peru; [email protected] (N.B.R.B.); [email protected] (J.O.S.L.); [email protected] (D.G.F.); [email protected] (M.O.); [email protected] (R.E.T.M.); [email protected] (E.B.C.); [email protected] (M.Á.B.G.) 2 Instituto de Ciencia de Datos, Universidad Nacional de Jaén, Jaén 06801, Peru; [email protected] * Correspondence: [email protected] (D.I.T.); [email protected] (R.S.L.); Tel.: +51-935-431-017 (D.I.T.); +51-956-715-497 (R.S.L.) Received: 27 October 2020; Accepted: 29 November 2020; Published: 1 December 2020

Abstract: Agricultural productivity in the Peruvian region of Amazonas is being jeopardized by conflicts and inadequate land use, that are ultimately contributing to environmental degradation. Therefore, our aim is to assess land suitability for potato (Solanum tuberosum L.) farming in the Jucusbamba and Tincas microwatersheds located in Amazonas, in order to improve land-use planning and enhance the crop productivity of small-scale farmers. The site selection methodology involved a pair-wise comparison matrix (PCM) and a weighted multicriteria analysis using the Analytical Hierarchy Process (AHP) on selected biophysical and socioeconomical drivers. Simultaneously, land cover mapping was conducted using field samples, remote sensing (RS), geostatistics and geographic information systems (GIS). The results indicated that for potato crop farming, the most important criteria are climatological (30.14%), edaphological (29.16%), topographical (25.72%) and socioeconomical (14.98%) in nature. The final output map indicated that 8.2% (22.91 km2) was highly suitable, 68.5% (190.37 km2) was moderately suitable, 21.6% (60.11 km2) was marginally suitable and 0.0% was not suitable for potato farming. Built-up areas (archaeological sites, urban and road networks) and bodies of water were discarded from this study (4.64 km2). This study intends to promote and guide sustainable agriculture through agricultural land planning.

Keywords: agricultural zoning; AHP-GIS; EMC; GIS modelling; kriging; land suitability; Solanum tuberosum

1. Introduction Potatoes (Solanum tuberosum L.) are a vital source of carbohydrates, proteins, vitamins and minerals, thus holding a fundamental role in the food chain [1]. In 2018, potato cultivation areas in China occupied approximately 5 million hectares, representing more than 20% of the world’s total potato farming area and making China the world’s top potato producer [2,3]. Peru, is the main potato producer within Latin America, and it is home to the largest diversity of potato varieties (about 3000 of the 4000 existing varieties) [4]. Moreover, the gross value of production (GVP) of potatoes represented

Agronomy 2020, 10, 1898; doi:10.3390/agronomy10121898 www.mdpi.com/journal/agronomy Agronomy 2020, 10, 1898 2 of 18

10.6% and 15% of the Peruvian gross value of agricultural production in 2016 and 2017, respectively, making it the second most important crop after rice [5]. However, global agriculture is struggling between the need to increase agricultural production and growing environmental concerns [6]. In addition, factors such as population growth, climate change, production technology difficulties, overexploitation of soil and supply problems complicate the ability to predict future potato production [7]. Another problem of Peruvian agricultural activity is deficient planning of potential territories for specific crops, which causes disturbances in the ecosystem, such as annual deforestation to install new plots or the overexploitation of territories which are not suitable for cultivation [8,9]. Therefore, agricultural production strategies and land suitability analyses that take into account climatological [5], topographical, geological and edaphological conditions [10,11] are necessary. Tools such as Remote Sensing (RS), Geographic Information Systems (GIS) and multicriteria analysis (MCA) techniques can be used to identify and prioritize suitable land for agriculture [10,12]. The MCA method used to determine the general suitability of land in Peru is the Classification of Lands by their Capacity of Greater Use (CTCUM) [13]. CTCUM is conditioned by the superposition of criteria of climate (Holdrige life zones [14]), geomorphology (terrain slope) and soil (edaphic criteria of a soil survey [15]). Lands are classified as being suitable for clean/intensive crops, permanent crops, pastures, silviculture, and protection/conservation [13]. However, CTCUM has been applied only in 15/25 Peruvian regions (as part of the Ecological and Economic Zoning–ZEE) and other small districts and watersheds [16]. Another method that will contribute to the CTCUM is Agroecological Zoning (ZAE), whose methodological guide is currently in the process of being approved by the Ministry of Agriculture (MINAGRI). ZAE is also an EMC method that originated with FAO [17] to determine the suitability of land for specific crops. However, like CTCUM, ZAE does not consider the relative importance of the MCA criteria, the incorporation of which is important to improve the EMC. In this context, the analytical hierarchy process (AHP) is the most widely used; it consists of estimating the importance of each criterion using expert opinions. The AHP technique, used to resolve hierarchically structured decision making problems at different levels, seeks to estimate the relative weight of criteria [18,19]. AHP integrated with GIS allows the use of multilevel hierarchies comprising different criteria and restrictions, and is one of the most promising methods for land suitability analysis [10]. Likewise, AHP coupled with RS-GIS has been used in numerous studies worldwide for land suitability analyses for potato farming [20–23], other crops [11,12,24–32] and agriculture in general [7,10,33–35]. In Peru, with this method, only two previous studies have reported suitable land analyses for specific crops: for Malus domestica Borkh. in the Mala Valley, Lima Region [36] and for Coffea arabica L. in the Amazonas Region [8]. This study analyzes land suitability for potato (Solanum tuberosum) cultivation in the Jucusbamba and Tincas microwatersheds (Amazonas, NW Peru), using the AHP and RS–GIS approach, aiming to improve land-use planning in the microwatershed area and enhance the crop productivity of small-scale farmers. In sum, (i) climatological, topographical, socioeconomical and edaphological criteria were mapped and weighed, and (ii) through the use of weighted overlay techniques, suitable potato cultivation areas were identified. Our study hypothesized that the area modelled as “highly suitable” for potato cultivation would be greater than the current agricultural area being used for this purpose. This study is intended to provide an instrument in the decision-making process of land-use planning, as well as to promote and guide sustainable agriculture.

2. Materials and Methods

2.1. Study Area

This study was carried out in two microwatersheds (6◦030–6◦170 S, 78◦040–77◦540 W) that included the territories of the districts of Inguilpata, Lonya Chico, Trita, Luya, Lámud and San Cristóbal, located in the province of Luya (Amazonas Region, NW Peru), at an altitudinal gradient of 1435–3433 m a.s.l (Figure1). Luya has a population of 3579 inhabitants and an economy based on livestock, commerce and small AgronomyAgronomy 20202020, 10,, 10x ,FOR 1898 PEER REVIEW 3 of3 18of 18 km2), corn, legumes, wheat and barley [38]. Within Luya, the Jucusbamba and Tincas scale agriculture [37] that primarily produces coﬀee (84.90 km2), potatoes (18.45 km2), corn, legumes, 2 microwatershedswheat and barley cover [38]. approximately Within Luya, the 278.03 Jucusbamba km and and are Tincas the microwatershedsmost representative cover potato-producing approximately areas.278.03 However, km2 and due are theto the most lack representative of spatial planning potato-producing for agricultural areas. However, activities, due the to the area lack is of threatened spatial by deforestation,planning for agricultural land use confli activities,cts and the soil area degradation, is threatened byjeopardizing deforestation, productivity land use conﬂicts [9,39]. andAccording soil to thedegradation, CTCUM, jeopardizing the microwatersheds productivity [9possess,39]. According land suitable to the CTCUM, mainly the for microwatersheds pastures (147.97 possess km2), permanentland suitable crops mainly (51.95 for km pastures2), silviculture (147.97 km 2(49.16), permanent km2), cropsprotection/conservation (51.95 km2), silviculture (24.81 (49.16 km km2)2 ),and clean/intensiveprotection/conservation crops (2.88 (24.81 km2) km [40].2) and Additionally, clean/intensive the crops study (2.88 area km has2)[ 40three]. Additionally, microclimates the study (humid temperatearea has cold, three warm microclimates temperate (humid slightly temperate humid and cold, warm warm temperate temperate humi slightlyd in humid the upper, and warm middle andtemperate lower parts humid of the in basin, the upper, respectively) middle and [41], lower with parts mean of theannual basin, temperature respectively) and [41 rainfall], with meanof 15 °C andannual 1015 mm, temperature respectively and rainfall [42]. of 15 ◦C and 1015 mm, respectively [42].

FigureFigure 1. Location 1. Location of ofthe the Jucusbamba Jucusbamba an andd Tincas Tincas microwatersheds microwatersheds in in the the province province of of Luya, Luya, Amazonas Amazonas (NW(NW Peru). Peru).

2.2. Methodology Framework 2.2. Methodology Framework Figure2 shows the applied research framework for land suitability analyses with respect to Figure 2 shows the applied research framework for land suitability analyses with respect to potato (S. tuberosum) cultivation using a AHP and RS-GIS approach in the Jucusbamba and Tincas potatomicrowatersheds. (S. tuberosum) To cultivation summarize, using (i) criteria a AHP that and could RS-GIS determine approach potato in growth the Jucusbamba and production and were Tincas microwatersheds.identified and selected; To summarize, (ii) these (i) criteria criteria were that mapped could determine using field potato and RS growth data in aand GIS production environment; were identifiedand (iii) and the selected; criteria were (ii) comparedthese criteria to determine were mapped their degreeusing field of importance and RS data using in AHP.a GIS Then, environment; using andGIS, (iii) mapsthe criteria were integrated were compared and reclassified to determine according their to degree suitability of importance thresholds using using weighted AHP. Then, overlay. using GIS, maps were integrated and reclassified according to suitability thresholds using weighted overlay.

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Identification and selection of criteria and sub-criteria Identification and selection of criteria and sub-criteria

Climatological Topographical Socioeconomical Edaphological Climatological Topographical Socioeconomical Edaphological

GIS–RS AHP

GIS–RS CRAHP > 10% Cartographic Hierarchy PairwiseCR > 10% Is the matrix Sub-criteria Obtaining Cartographicconditioning developmentHierarchy comparisonPairwise Isconsistent? the matrix Sub-criteriamap baseObtaining mapping - conditioningGrid 10 m development comparison consistent? generationmap base mapping -- GridUTM 10 18 m South No Approve criteria weights CR < 10% generation - UTM 18 South No Approve criteria weights CR < 10%

Spatial analysis using GIS Spatial analysis using GIS Extract Climatological suitability map - Roads Sub-criteria map Weighted Weighted Extract Climatological suitability map -- RoadsRivers Sub-criteriareclassification map Weightedoverlay of Topographical suitability map Weightedoverlay of -- RiversMain urban zones basedreclassification on suitability overlaysecond of SocioeconomicalTopographical suitability suitability map map overlayfirst of of third hierarchy hierarchy hierarchy -- MainBuffer urban 200m zones from based on suitability second SocioeconomicalEdaphological suitabilitysuitability map map first archaeological sites of third hierarchy hierarchy hierarchy - Buffer 200m from Edaphological suitability map archaeological sites

Goal Goal Land suitability for potato cultivation Land suitability for potato cultivation FigureFigure 2. Research 2. Research framework framework for for land land suitability suitability modelling for for potato potato cultivation cultivation in the in Jucusbamba the Jucusbamba and TincasFigureand Tincas 2. microwatersheds Research microwatersheds framework (Amazonas, (Amazonas, for land suitability NW NW Peru).Peru). modelling for potato cultivation in the Jucusbamba and Tincas microwatersheds (Amazonas, NW Peru). 2.3. Identiﬁcation2.3. Identification of Criteria of Criteria and and Subcriteria Subcriteria 2.3. Identification of Criteria and Subcriteria The identiﬁcationThe identification process process of suitableof suitable land land for for potato potato cultivation cultivation is is based based on on favourable favourable criteriacriteria with The identification process of suitable land for potato cultivation is based on favourable criteria regardwith to cropregard growth to crop and growth production. and production. Criteria selection Criteria was selection carried was out usingcarried technical out using manuals technical [43 –45] with regard to crop growth and production. Criteria selection was carried out using technical and cropmanuals suitability [43–45] analysisand crop articles suitability [20,21 analysis,46,47]. articles Subsequently, [20,21,46,47]. a hierarchy Subsequently, composed a hierarchy of four main manuals [43–45] and crop suitability analysis articles [20,21,46,47]. Subsequently, a hierarchy criteriacomposed (climatological, of four main edaphological, criteria (climatological, topographical edaphological, and socioeconomical) topographical and and sixteen socioeconomical) subcriteria was composedand sixteen of subcriteria four main wascriteria made, (climatological, based on the edaphological, accessibility and topographical relevance ofand the socioeconomical) data within the made, based on the accessibility and relevance of the data within the biophysical and socioeconomic andbiophysical sixteen andsubcriteria socioeconomic was made, conditio basedns on of the studyaccessibility area (Figure and relevance 3). of the data within the conditionsbiophysical of the and study socioeconomic area (Figure conditio3). ns of the study area (Figure 3).

Goal Criteria Sub–criteria Alternative Sub–criteria Alternative Goal FirstCriteria hierarchy Second hierarchy Third hierarchy First hierarchy Second hierarchy Third hierarchy Mean annual temperature Climatological Mean annual temperatureprecipitation Climatological Mean annual precipitation Elevation Topographical TerrainElevation slope Topographical Terrain aspectslope Reclassification based on suitability: Terrain aspect Reclassification based Land Use/Land Cover on suitability: (4) Very suitable Socioeconomical Land Use/Land Cover Land suitability Distance to rivers (4)(3) VeryModerately suitable suitable Landfor suitability potato Socioeconomical DistanceDistance to to rivers roads (3)(2) ModeratelyMarginally suitable cultivation for potato Distance to roads (2)(1) MarginallyNot suitable suitable cultivation Texture (1) Not suitable TexturepH Or ganicpH matter Or ganicNitrogen matter Edaphological PhNitrogen os ph or us Edaphological PhPotassium os ph or us Cation Potassiumexchange capacity CationElectrical exchange conductivity capacity Electrical conductivity

Figure 3. Variable hierarchization for land suitability modelling for potato cultivation in the Figure 3. Variable hierarchization for land suitability modelling for potato cultivation in the Jucusbamba FigureJucusbamba 3. Variable and Tincas hierarchization microwatersheds for land (Amazonas, suitability NW modelling Peru). for potato cultivation in the and TincasJucusbamba microwatersheds and Tincas microwatersheds (Amazonas, NW (Amazonas, Peru). NW Peru).

2.4. Datasets Sources and Processing Temperature and precipitation layers were obtained from the WorldClim 2.0 database, which contains

2 the average monthly climate data from 1970 to 2000 with a spatial resolution of 30 s (about 1 km )[42]. Elevation, slope and aspect terrain layers were generated from the Digital Elevation Model (DEM, Agronomy 2020, 10, 1898 5 of 18

ALOS PALSAR, 12.5 m), downloaded from the Distributed Active Archive Center (DAAC) from the Alaska Satellite Facility (ASF) [48]. Land Use/Land Cover (LULC) layers were obtained using a Sentinel 2A image (Path/Row: 17MRP; 20 June 2020; Bands: 2–8A, 11–12), acquired from the Copernicus Services Data Hub platform operated by the European Spatial Agency (ESA). Image preprocessing (i.e., atmospheric correction and resampling at 10 m) was executed using the Sentinel Application Platform (SNAP). Afterwards, digital processing was done in ENVI 5.3, following the methodology established by Murillo and Ortiz [49] and CORINE Land Cover adapted for Perú [50]. Hence, LULC were organized into five classes: urban area, grasslands, pastures and crops, bodies of water and forests. To assess the thematic accuracy, a 195–point confusion matrix indicated a general precision of 97% and a Kappa coefficient of 0.96. The road network of the Ministry of Transport and Communications (MTC) was used as a reference for road distribution [51]. This vectoral layer was updated through field explorations. Furthermore, distance to roads and rivers was calculated using the Euclidean distance tool. The base layer of the rivers was obtained from the national charts 13 g and 13 h, downloaded from the Ministry of Education (MINEDU) geoportal [52]. Edaphological layers were generated from data obtained by collecting 72 soil samples at a depth of 30 cm throughout the study area (Figure1). These 72 samples were calculated and distributed in accordance with the regulations of the MINAGRI [15], based on spatial data on physiography, life zones, geology and geomorphology obtained from the ZEE–A [53]. Such samples were analysed at the Water and Soil Research Laboratory (LABISAG) of the Research Institute for Sustainable Development of Ceja de Selva (INDES-CES), that belongs to the Toribio Rodriguez de Mendoza National University (UNTRM). The physical (sand, silt and clay content) and chemical (pH, Soil Organic Matter—SOM, Nitrogen—N, Phosphorus—P, Potassium—K, Electrical Conductivity—EC, and Cation Exchange Capacity—CEC) properties of the soil were analyzed. Subsequently, all physical chemical properties were interpolated using the Ordinary Kriging (OK) method (in ArcGIS 10.5) based on five semivariogram models (Spherical, Circular, Gaussian, Linear and Exponential) [21,24,25,27,32]. In order to determine the most suitable semivariogram for each property, the coefficient of determination (R2), mean bias error (MBE), mean absolute bias error (MABE), root-mean-square error (RMSE) and the Student’s t-distribution were used (see formulas in Quiñones et al. [54]). For this purpose, of the 72 samples, 14 (20%) were chosen for the validation stage due to their homogenous distribution. In case any property did not result in an acceptable performing model (see statistical interpretation in Quiñones et al. [54]), layers (250 m; average depths 0–5, 5–10 and 10–15 cm) from the SoilGrids global digital soil mapping system were considered [55]. Additionally, according to the classification given by the United States Department of Agriculture (USDA), soil texture data were generated by using sand, silt and clay content layers along with the QGIS raster calculator [56].

2.5. Standardization of Subcriteria Layers through Suitability Thresholds In order to execute weighted overlay, the units of the subcriteria were standardized using the raster reclassification of their maps. [57]. For this purpose, each continuous map (subcriterion) was divided according to land suitability level [17,58]. The five levels which are most commonly used in this process are taken from “A Framework For Land Evaluation” [59], and are as follows: “highly suitable“ (territory with minor limitations for agricultural use that do not affect productivity or significantly increase expenses), “moderately suitable“ (territory with moderate limitations that reduce productivity or imply slight risks of soil degradation), “marginally suitable“ (territory with severe limitations that reduce productivity or increase expenses that will only be marginally justified), “currently unsuitable“ (territory whose limitations can be eliminated with technical means or costs, although these modifications are currently unthinkable) and “permanently unsuitable“ (territory with serious limitations that are supposed to be insurmountable in the long term). However, in this study, as in many other studies [8,16], the last two levels (N1 and N2) were combined and four levels of land suitability for potato cultivation were determined (1–4), with 1 being “not suitable” and Agronomy 2020, 10, 1898 6 of 18

4 “highly suitable” [60]. Climatological, topographical, socioeconomical and edaphological criteria were standardized according to suitability thresholds reported in previous studies (Table1)[ 33,44,46]. Agronomy 2020, 10, x FOR PEER REVIEW 7 of 18 Lastly, standardized thematic maps for each subcriterion are shown in Figure4.

Figure 4. Thematic maps for climatological subcriteria (a,b), topographical subcriteria (c–e) and Figuresocioeconomic 4. Thematic subcriteria maps (ffor–h )climatological for land suitability subcriteria modelling (a,b for), topographical potato cultivation subcriteria in the Jucusbamba (c–e) and socioeconomicand Tincas microwatersheds subcriteria (f–h (Amazonas,) for land suitability NW Peru). modelling for potato cultivation in the Jucusbamba and Tincas microwatersheds (Amazonas, NW Peru). Table 1. Subcriteria reclassiﬁcation and punctuation for land suitability modelling for potato cultivation 2.6. Analyticalin the Jucusbamba Hierarchy and Process Tincas (AHP) microwatersheds (Amazonas, NW Peru).

Highly Moderately Marginally TheCriteria AHP/Subcriteria is an accurate approach for quantifying the importanceNot weight Suitable for (1) each2 Adapted criterion from and Suitable (4) 2 Suitable (3) 2 Suitable (2) 2 subcriterion; the sum of the importance weight of each hierarchy group (Figure 3) is equal to 1 [12]. Climatological RelativeMean annualimportance temperature was (◦C) estimated 9–11 using pairwise 11–15 comparison 15–18 matrices (PCM)<9/>18 [31,66], [29completed,30,46,61,62] by 1000–800 800–500 <500 a groupMean annualof experts precipitation based (mm) on Saaty’1000–1200s nine-point ratio scale [19] (Table 2). This group of [experts20,43,46] was 1200–1300 1300–1400 >1400 made up of researchers in potato cultivation fromTopographical the UNTRM and the National Institute for Agrarian 2300–2000 2000–1500 <1500 InnovationElevation (INIA). (m a.s.l.) 2600–2300 [29,43,46] 2600–3500 3500–4500 >4500 Slope (◦) 0–12 13–18 18–25 >25 [24,30,43,46] AspectTable 2. Saaty scale SW, for plana pairwise comparison S, SE between E, W the criteria NW, in NE,the NAHP. [33,35]

1/9 1/7 1/5 1/3 1 3 5 7 9 Extremely Far Slightly Equally Slightly Far Extremely Less important important More important

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Table 1. Cont.

Highly Moderately Marginally Criteria/Subcriteria Not Suitable (1) 2 Adapted from Suitable (4) 2 Suitable (3) 2 Suitable (2) 2 Socioeconomical Urban areas and LULC Forest Pastures and crop Grasslands [63] bodies of water Distance to rivers (km) <0.5 0.5–1.5 1.5–2 >2 [33] Distance to roads (km) <0.5 0.5–1.5 1.5–3.0 >3.0 [33] Edaphological Texture 1 L, SL, SiL CL, SiCL, SC Si, LS, SiC S, C [43] pH 5.5–7 5–5.5/7–7.5 4–5/7.5–8 <4 / >8 [46,64] SOM (%) 5–10 5–2.5 2.5–1 <1 [46,64] N (%) >0.30 0.225–0.30 0.125–0.225 <0.125 [46,64,65] P (ppm) 20–40 15–20/>40 15–10 <10 [43,64] K (ppm) >350 200–350 100–200 <100 [21,46,64] CEC (cmol(+)/kg) 30–50 50–60/30–20 60–70/20–10 >70/<10 [64] EC (dS/m) <4 4–6 6–8 >8 [21,64] 1 L: Loam; SL: Sandy Loam; SiL: Silt Loam; CL: Clay Loam; SiCL: Silty Clay Loam; SC: Sandy Clay; S: Sand; C: Clay; SiC: Silty Clay; LS: Loamy Sand; Si: Silt. 2 Pixel value of the map reclassiﬁed according to the four levels of land suitability.

2.6. Analytical Hierarchy Process (AHP) The AHP is an accurate approach for quantifying the importance weight for each criterion and subcriterion; the sum of the importance weight of each hierarchy group (Figure3) is equal to 1 [ 12]. Relative importance was estimated using pairwise comparison matrices (PCM) [31,66], completed by a group of experts based on Saaty’s nine-point ratio scale [19] (Table2). This group of experts was made up of researchers in potato cultivation from the UNTRM and the National Institute for Agrarian Innovation (INIA).

Table 2. Saaty scale for pairwise comparison between the criteria in the AHP.

1/9 1/7 1/5 1/3 1 3 5 7 9

Extremely Far Slightly Equally Slightly Far Extremely Less importantimportant More important

Although experts’ opinions are highly valued, their subjective preferences may lead to inconsistencies [67,68]. Therefore, a consistency ratio (CR) must be calculated (Consistency Index (CI) / Random Index (RI) of the matrix) in order to establish a maximum inconsistency value. RI is calculated for diﬀerent “n” values (number of factors in the matrix) from a simulation of 100,000 matrixes (Table3)[ 69]. Also, CI serves as a measure of logical inconsistency against expert judgment during peer-to-peer comparisons [70]. For this purpose, the matrix with the highest value (λmax) is greater than or equal to the number of rows and columns (n) (Equation (1)) [34]. Moreover, CR must be <0.1 (less than 10% inconsistency) to be acceptable. Matrices with two subcriteria are automatically assumed to be consistent and acceptable [68]. This was the case for the climatological matrix.

CI = (λmax n)/(n 1), (1) − −

Table 3. Random Index (RI) value for each “n” in the AHP.

n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 IA 0 0 0.525 0.882 1.115 1.252 1.341 1.404 1.452 1.484 1.513 1.535 1.555 1.570 1.583 1.595 Agronomy 2020, 10, 1898 8 of 18

2.7. Weighted Overlay of Thematic Maps A weighted overlay analysis using QGIS was carried out with standardized thematic maps (GRIDi) and weight importance (WEIGHTi) for each subcriterion i. Each raster-based layer (Figure4) was integrated using Equation (2) [68] to generate ﬁnal output land suitability maps for potato cultivation (GRIDresult). GRIDresult = Σ [(GRIDi)(WEIGHTi)] (2)

3. Results

3.1. Descriptive Statistics for Soil Properties Table4 shows the descriptive statistics of soil properties in the study area. Moderate variability was shown by pH, while other physical chemical properties displayed a high variability in their results. Furthermore, a negative skewness was observed for pH, Log P, CEC and Sand, and a slight positive skewness for EC, SOM, Log SOM, N, P, K, Silt and Clay.

Table 4. Descriptive statistical analysis of soil properties in the study area (n = 72) in the Jucusbamba and Tincas microwatersheds (Amazonas, NW Peru).

EC SOM Log N P K Log CEC Sand Silt Clay pH (dS/m) (%) SOM (%) (ppm) (ppm) P (cmol(+)/kg) (%) (%) (%) Mean 6.86 0.23 5.82 0.71 0.26 9.26 207.29 0.74 24.86 48.52 17.73 33.75 Median 7.63 0.14 5.48 0.74 0.25 5.20 194.00 0.72 30.50 50.00 16.00 30.60 Minimum 2.84 0.01 1.41 0.15 0.04 0.28 26.40 0.55 4.00 10.00 5.30 7.30 − Maximum 8.79 0.66 35.30 1.55 0.54 66.30 476.19 1.82 39.05 82.00 56.00 78.70 Std. dev 1.54 0.18 4.05 0.22 0.11 10.89 114.05 0.47 11.77 18.58 7.27 18.05 CV (%) 1 22.47 78.26 69.70 44.13 117.51 55.02 47.33 38.30 41.01 53.47 Skewness 2 0.67 0.53 5.50 0.08 0.24 3.11 0.34 0.44 0.57 0.27 2.06 0.48 − − − − Kurtosis 0.95 0.99 37.11 2.55 0.53 11.54 0.72 0.55 1.31 0.77 9.00 0.65 − − − − − − − 1 Coeﬃcient of variation: <15 = low variation, 15–35 = moderate variation, >35 = high variation [21,29,30]. 2 Skewness: <| + 0.5| = normal distribution, 0.5–1.0 = application of character changing for dataset, and >1, 0 application of logarithmic− change [21]. →

Fifty Ordinary Kriging (OK) spatial interpolation models with ﬁve semivariogram models were generated for each soil property. Performance statistics were calculated for all models and the model with the best performance for each physicochemical property was chosen (Table5). The Gaussian semivariogram model was better adjusted to diﬀerent soil properties. Properties with extremely low R2 (N, SOM and Silt) were excluded. Therefore, the silt layer was calculated by supplementing the Sand + Clay layer to 100, and N and SOM layers were obtained from SoilGrids. Lastly, thematic maps for each edaphological subcriteria are shown in Figure5.

Table 5. Best performing semivariogram model for each physical chemical soil property in the Jucusbamba and Tincas microwatershed (NW Peru).

Physical Chemical Semivariogram R2 MBE MABE RMSE t-Student Property Model pH Gaussian 0.650 0.199 0.873 1.064 0.505 N Gaussian 0.078 0.073 0.105 0.121 0.018 P Spherical 0.299 2.157 7.742 9.165 0.398 K Linear 0.403 20.078 100.195 116.222 0.538 SOM Gaussian 0.081 1.444 2.102 2.408 0.018 EC Gaussian 0.393 0.098 0.154 0.178 0.035 CEC Exponential 0.796 -0.769 5.643 6.841 0.690 Sand Gaussian 0.481 -0.247 11.516 14.844 0.953 Clay Exponential 0.607 1.560 11.252 12.824 0.666 Silt Linear 0.021 -1.666 4.951 6.053 0.321 Agronomy 2020, 10, x FOR PEER REVIEW 9 of 18

Sand + Clay layer to 100, and N and SOM layers were obtained from SoilGrids. Lastly, thematic maps for each edaphological subcriteria are shown in Figure 5.

Table 5. Best performing semivariogram model for each physical chemical soil property in the Jucusbamba and Tincas microwatershed (NW Peru).

Physical Chemical Semivariogram Model R2 MBE MABE RMSE t-Student Property pH Gaussian 0.650 0.199 0.873 1.064 0.505 N Gaussian 0.078 0.073 0.105 0.121 0.018 P Spherical 0.299 2.157 7.742 9.165 0.398 K Linear 0.403 20.078 100.195 116.222 0.538 SOM Gaussian 0.081 1.444 2.102 2.408 0.018 EC Gaussian 0.393 0.098 0.154 0.178 0.035 CEC Exponential 0.796 -0.769 5.643 6.841 0.690 Sand Gaussian 0.481 -0.247 11.516 14.844 0.953 Agronomy 2020, 10Clay, 1898 Exponential 0.607 1.560 11.252 12.824 0.666 9 of 18 Silt Linear 0.021 -1.666 4.951 6.053 0.321

. Figure 5. Thematic maps for each edaphological sub criterion for land suitability modelling for potato cultivationFigure 5. Thematic in the Jucusbamba maps for each and edaphological Tincas microwatersheds sub criterion (Amazonas, for land suitability NW Peru). modelling for potato cultivation in the Jucusbamba and Tincas microwatersheds (Amazonas, NW Peru). 3.2. Subcriteria Importance Weights Ten experts were consulted and 50 PCMs were constructed, one at the criteria level and four at the subcriteria level, for each expert. The average weighting for each climatological, topographical, socioeconomical and edaphological criterion and subcriterion is shown in Table6. These were standardized and scored according to their inﬂuence on potato cultivation in the Jucusbamba and Tincas microwatersheds. According to results shown, climatological (30.14%) and edaphological (29.16%) criteria hold the highest importance. Likewise, precipitation (0.4334), slope (0.5475), LULC (0.4764) and SOM (0.1654) are the most representative subcriteria for each hierarchical group. Moreover, average CR oscillated between 0.002 and 0.041 (Table7), which indicates that the matrices are consistent and acceptable. Agronomy 2020, 10, 1898 10 of 18

Table 6. Importance weights for each criteria and subcriteria for land suitability modelling for potato cultivation in the Jucusbamba and Tincas microwatersheds, Amazonas (NW Peru).

Standardized Standardized Criteria Weight Ranking Subcriteria Weight Ranking Weight Ranking Temperature 42.08 3 12.68 2 Climatological 30.14 1 Precipitation 57.92 1 17.46 1 Slope 54.75 1 14.08 2 Topographical 25.72 3 Elevation 31.06 2 7.99 4 Aspect 14.20 3 3.65 13 LULC 47.64 1 7.14 5 Socioeconomical 14.98 4 Distance to roads 26.56 3 3.98 9 Distance to rivers 25.80 2 3.86 12 Texture 9.24 8 2.69 16 pH 14.49 2 4.23 7 SOM 16.51 1 4.81 6 N 13.98 3 4.08 8 Edaphological 29.16 2 P 13.43 4 3.92 10 K 13.42 5 3.91 11 EC 9.42 7 2.75 15 CEC 9.52 6 2.78 14

Table 7. Consistency Ratio (CR) of Pairwise Comparison Matrix (PCM).

Climatological Topographical Socioeconomical Edaphological Ratio Criteria Subcriteria Subcriteria Subcriteria Subcriteria n 4 2 3 4 8 RI 0.882 0.00 0.525 0.882 1.404 λmax 3.992 - 3.042 4.263 8.403 CI 0.031 - 0.021 0.088 0.058 CR 0.037 Consistent and acceptable 0.040 0.022 0.041

3.3. Suitability Submodels for Potato Cultivation Suitability submodels were generated with the weighted overlap (Equation (2)) of the subcriteria for each hierarchical group (Figure6). The submodels that presented the largest “highly suitable” surface for potato cultivation were socioeconomical (96.41 km2) and topographical (89.65 km2) (Table8). In contrast, the submodels with the smallest “highly suitable” area were climatological (34.30 km2) and edaphological (12.72 km2). In order not to overestimate the suitability of the criteria maps, submodels and ﬁnal model, the road network, the main urban centres, rivers and a 200 m buﬀer zone of archaeological sites (4.64 km2) were excluded from the analysis.

Table 8. Land suitability according to criteria and subcriteria for potato cultivation in the Jucusbamba and Tincas microwatersheds (NW Peru).

Highly Moderately Marginally Not Suitable Criteria/Subcriteria Suitable (4) Suitable (3) Suitable (2) (1) km2 % km2 % km2 % km2 % Climatological 34.30 12.3 217.64 78.3 21.45 7.7 0.00 0.0 Temperature 47.39 17.0 140.18 50.4 84.46 30.4 1.36 0.5 Precipitation 105.98 38.1 104.65 37.6 47.83 17.2 14.92 5.4 Topographical 89.65 32.2 72.83 26.2 72.22 26.0 38.69 13.9 Slope 102.76 37.0 58.79 21.1 45.61 16.4 66.23 23.8 Elevation 92.17 33.2 107.53 38.7 68.73 24.7 4.96 1.8 Aspect 27.86 10.0 82.27 29.6 77.32 27.8 85.95 30.9 Socioeconomical 96.41 34.7 124.15 44.7 50.68 18.2 2.15 0.8 LULC 129.61 46.6 55.75 20.1 51.17 18.4 36.86 13.3 Distance rivers 157.05 56.5 71.70 25.8 34.42 12.4 10.22 3.7 Distance to roads 117.61 42.3 75.42 27.1 58.83 21.2 21.53 7.7 Agronomy 2020, 10, 1898 11 of 18

Table 8. Cont.

Highly Moderately Marginally Not Suitable Criteria/Subcriteria Suitable (4) Suitable (3) Suitable (2) (1) km2 % km2 % km2 % km2 % Edaphological 12.72 4.6 126.52 45.5 134.15 48.2 0.00 0.0 Texture 92.14 33.1 18.17 6.5 105.61 38.0 57.47 20.7 pH 104.39 37.5 112.77 40.6 56.23 20.2 0.00 0.0 SOM 129.63 46.6 143.64 51.7 0.07 0.0 0.05 0.0 N 261.94 94.2 11.38 4.1 0.06 0.0 0.01 0.0 P 49.47 17.8 41.66 15.0 46.49 16.7 135.78 48.8 K 0.83 0.3 173.51 62.4 99.06 35.6 0.00 0.0 CEC 59.48 21.4 116.02 41.7 82.37 29.6 15.52 5.6 CE 83.77 30.1 97.03 34.9 91.99 33.1 0.60 0.2 Agronomy 2020, 10, x FOR PEER REVIEW 11 of 18

Figure 6. Submodels of land suitability for potato cultivation in the micro-basins of Jucusbamba and Tincas,Figure 6. Amazonas Submodels (NW of land Peru). suitability for potato cultivation in the micro-basins of Jucusbamba and 3.4. LandTincas, Suitability Amazonas Model (NW Peru).

TableThe ﬁnal 7. Land model suitability of land according suitability to criteria for and potato subcriteria cultivation for potato was cultivation generated in withthe Jucusbamba the weighted superpositionand Tincas of microwatersheds the submodels (Figure(NW Peru).7). Of the microbasin area, 8.2% (22.91 km 2) was “highly suitable” for potato cultivation, “moderately suitable” (190.37 km2) and “marginally suitable” (60.11 km2) areas Highly Moderately Marginally represented 90.1%, and there were no “not suitable” areas found. Not Suitable (1) Criteria/Subcriteria Suitable (4) Suitable (3) Suitable (2) km2 % km2 % km2 % km2 % Climatological 34.30 12.3 217.64 78.3 21.45 7.7 0.00 0.0 Temperature 47.39 17.0 140.18 50.4 84.46 30.4 1.36 0.5 Precipitation 105.98 38.1 104.65 37.6 47.83 17.2 14.92 5.4 Topographical 89.65 32.2 72.83 26.2 72.22 26.0 38.69 13.9 Slope 102.76 37.0 58.79 21.1 45.61 16.4 66.23 23.8 Elevation 92.17 33.2 107.53 38.7 68.73 24.7 4.96 1.8 Aspect 27.86 10.0 82.27 29.6 77.32 27.8 85.95 30.9 Socioeconomical 96.41 34.7 124.15 44.7 50.68 18.2 2.15 0.8 LULC 129.61 46.6 55.75 20.1 51.17 18.4 36.86 13.3 Distance rivers 157.05 56.5 71.70 25.8 34.42 12.4 10.22 3.7 Distance to roads 117.61 42.3 75.42 27.1 58.83 21.2 21.53 7.7 Edaphological 12.72 4.6 126.52 45.5 134.15 48.2 0.00 0.0 Texture 92.14 33.1 18.17 6.5 105.61 38.0 57.47 20.7

Agronomy 2020, 10, x FOR PEER REVIEW 12 of 18

pH 104.39 37.5 112.77 40.6 56.23 20.2 0.00 0.0 SOM 129.63 46.6 143.64 51.7 0.07 0.0 0.05 0.0 N 261.94 94.2 11.38 4.1 0.06 0.0 0.01 0.0 P 49.47 17.8 41.66 15.0 46.49 16.7 135.78 48.8 K 0.83 0.3 173.51 62.4 99.06 35.6 0.00 0.0 CEC 59.48 21.4 116.02 41.7 82.37 29.6 15.52 5.6 CE 83.77 30.1 97.03 34.9 91.99 33.1 0.60 0.2

3.4. Land Suitability Model The final model of land suitability for potato cultivation was generated with the weighted superposition of the submodels (Figure 7). Of the microbasin area, 8.2% (22.91 km2) was "highly suitable"Agronomy 2020 for, potato10, 1898 cultivation, “moderately suitable” (190.37 km2) and “marginally suitable” 12(60.11 of 18 km2) areas represented 90.1%, and there were no “not suitable” areas found.

Figure 7. LandLand Suitability Suitability Map Map for for potato potato cultivation cultivation in in the the Jucusbamba Jucusbamba and and Tincas Tincas microwatersheds, microwatersheds, Amazonas (NW Peru). 4. Discussion 4. Discussion The coupled AHP and RS-GIS approach is widely used in agricultural land zoning processes. The coupled AHP and RS-GIS approach is widely used in agricultural land zoning processes. Nevertheless, in comparison to previous studies of the suitability of the territory for potato Nevertheless, in comparison to previous studies of the suitability of the territory for potato cultivation [20–23,46,47,63], for other crops (Brassica napus L. [11,25], Glycine max L. [11], Triticum cultivation [20–23,46,47,63], for other crops (Brassica napus L. [11,25], Glycine max L. [11], Triticum aestivum L. [23,27], Zea mays L. [27,29], Oryza sativa L. [28], coffee [31], and Vicia faba L. [32]) or for aestivum L. [23,27], Zea mays L. [27,29], Oryza sativa L. [28], coffee [31], and Vicia faba L. [32]) or for agriculture in general [7,10,33–35], we included a greater number of subcriteria. However, in this type agriculture in general [7,10,33–35], we included a greater number of subcriteria. However, in this type of study, the number of subcriteria depends on the focus of the study and the availability of spatial data. of study, the number of subcriteria depends on the focus of the study and the availability of spatial For example, future studies may include economic subcriteria (cost benefit, productivity, crop return data. For example, future studies may include economic subcriteria (cost benefit, productivity, crop rate or others [71]) and social (level of family skills, availability of labour, access to information, poverty return rate or others [71]) and social (level of family skills, availability of labour, access to information, rate or others [24]) that were not considered here due to the lack of spatial data [68]. An attempt was poverty rate or others [24]) that were not considered here due to the lack of spatial data [68]. An made to overcome this difficulty by generating larger-scale soil maps through field sampling and the application of geostatistics. Like other studies on the suitability of the territory for the cultivation of potato [21,22] or other crops [25,27,29,30,32], geostatistics were applied, being among the most suitable interpolation methods for the estimation of soil properties [32,72]. Ordinary Kriging (OK) interpolation was used and the Gaussian semivariogram model was best adjusted to different soil properties. For Tashayo et al. [29,30], Gaussian was also the best fit model, while for Ostovari et al. [25] and Pilevar et al. [27], it was Spherical. Although OK is the most widely used method and several studies [21,27,29,30,72] have reported that it is the best for mapping soil characteristics, Li et al. [73] noted that it has a smoothing effect and tends to neglect local variability. In this regard, the Regression Kriging (RK) and other more advanced methods take advantage of the spatial information of environmental covariates and exceed OK [73,74]. In future studies, it is suggested that more advanced spatial interpolation methods be implemented. In this study, we used Agronomy 2020, 10, 1898 13 of 18 models generated with low yields (Sand, K, EC and P) due to the lack of detailed secondary information for P and K, and because field sampling was considered better than the information at a smaller scale which was available for Sand and EC (in SoilGrids [55]). Additionally, after an extensive literature review, only four studies [20–23] included an integrated AHP approach in land suitability analyses for potato cultivation. Among these, three [21–23] evaluated edaphological subcriteria; the study of Kamau et al. did not [20]. This study indicated that the most important criteria for potato cultivation are climatological (30.14%) and edaphological (29.16%), followed by topographical (25.72%) and socioeconomical (14.98%). Similar results were reported by Kamau et al. [20], who did not consider socioeconomic criteria; for their study, edaphological criteria were the most important, followed by climatological and topographic. The similar level of importance of climatological and edaphological subcriteria was due to the fact that both strictly determine the development of crops [75]. Topographic subcriteria are of similar importance, since in mountainous ecosystems, like our study area, the topography has a great influence on the microclimates and edaphic properties (erosion and nutrient cycling) [7,24]. Of the climatological subcriteria, precipitation (57.92%) was the most important in this study, followed by temperature (42.08%). For Kamau et al. [20], precipitation was also more important than temperature. The great importance of precipitation was due to the high water needs of the potato crop (1000–1200 mm, well distributed during the growing cycle) [76]. The deficit of irrigation technologies in the province of Luya and in Amazonas influences the dependence on precipitation for the cultivation of potatoes and other crops. This dependence leads poorly informed residents to start fires with the purpose of generating rains during the dry season of the year [77], leading to land degradation. Topographical subcriteria (slope, elevation and aspect) were included in this study, unlike that of Kamau et al. [20], as they were taken into account in other agricultural zoning studies [12,26,33,35]. With regard to terrain aspect, this subcriterion strongly influenced solar light and microclimate temperature variations [78]. Slope (54.75%) was considered the most important topographical subcriterion in this study, since it influenced soil depth distribution, soil moisture, soil erosion, nutrient availability, LULC, etc. [79]. Therefore, Zolekar and Bhagat [7] suggested using a high-resolution DEM, which describes the topographic variations in detail and provides greater precision in determining the suitability of the territory for agriculture, especially in mountainous areas such as those in this study. With regards to edaphological subcriteria, SOM (16.51%) and pH (14.49%) were the most important, followed by primary macronutrients (N 13.98%, P 13.43% and K 13.42%) and other subcriteria (CEC 9.52%, EC 9.42% and Texture 9.24%). This differs from other land suitability analyses, like that of Kamau et al. [20], who used four edaphological subcriteria, whereby texture and drainage had higher importance values, followed by depth and pH. Furthermore, this study differed from studies carried out by Singha et al. [21] and Keshavarzi et al. [22], who used eight different subcriteria, from which K, texture, Organic Carbon (OC) and pH obtained higher importance values. On the other hand, Bagherzadeh et al. [23] obtained higher importance values for CEC and OC within the ten subcriteria they analyzed. The number and the different edaphological subcriteria used by previous studies, as well as each local reality and the varied experience of the experts, contributed to determining the importance of the edaphological subcriteria used in this study [8]. In fact, in any study on the suitability of land for agriculture, knowledge of the physicochemical properties of the soil is indispensable [29]. Within the socioeconomic subcriteria, LULC (47.64%) stands out on the distance to highways (26.56%) and distance to the water network (25.80%); this may have been due to the conservationist approach of the experts. This is because in the province of Luya, agriculture and livestock have brought about large changes in LULC, which caused a loss of 60.21 km2 between 2001–2018 (66–88% of the loss occurred in areas <1 ha) [80]. Additionally, the slightly greater importance of proximity to a road compared to proximity to a water network may have been due to the fact that the study area featured many river water resources, while it lacked good road access. However, this difficulty may be overcome with the paved road project that goes from Corral Quemado (exit to markets on the Agronomy 2020, 10, 1898 14 of 18 coast), passes through Cumba, Collonce, Cohechán, to Luya, and continues to Chachapoyas (capital of the region Amazonas) or to the markets of the coast (Chiclayo, etc.) and the jungle (Moyobamba, Tarapoto, etc.) [81]. In the Jucusbamba and Tincas microbasins, the area modeled as “highly suitable” (22.91 km2) for potato cultivation was 454.5% greater than the potato area reported in the last national agricultural census of 2012 (4.13 km2, adding areas of the districts that include the microwatersheds [35]) and spatially coincided with only 29.7% (6.81 km2) with the area classified as being suitable for crops (54.83 km2 adding classes clean/intensive crops and permanent crops [37]) of the CTCUM of the microwatersheds. Our methodology framework, that integrates AHP and RS-GIS in order to assess land suitability for potato cultivation, can be applied in various future studies with necessary adjustments. Likewise, this methodology can be applied to other crops that have nutritional, economic and environmental importance. Unlike CTCUM, a normative technical instrument that determines the general suitability of land, our framework makes it possible to analyze the suitability of land for specific crops, which could improve the zoning process. Furthermore, CTCUM is based on a Peru Life Zones map which is outdated, even at local scales. For its part, although the ZAE will be an advance in agricultural territorial planning in Peru because it can be used to evaluate specific crops, it is recommended that the weighting of the importance of the criteria through the AHP be implemented. This is important, since each agricultural reality (district, province, region or country) is different, and therefore, the criteria must be prioritized by local experts.

5. Conclusions This study assessed land suitability for potato cultivation by coupling AHP and RS-GIS techniques and basing them on four main criteria (climatological, edaphological, topographical and socioeconomical) and sixteen subcriteria. A thorough, expert-based analysis showed that climatological and edaphological were the most important criteria. Additionally, the resulting socioeconomical submodel possessed the largest “highly suitable” area in contrast to the edaphological submodel. A ﬁnal output land suitability map labeled 8.2% (22.91 km2) and 68.5% (190.37 km2) of the Jucusbamba and Tincas microwatersheds as “highly suitable” and “moderately suitable” for potato cultivation, respectively. This research will provide support in the decision-making process for sustainable agricultural production in the Jucusbamba and Tinas microwatersheds by giving local farmers tools to improve agricultural planning. Finally, this study is intended to promote and guide sustainable agriculture.

Author Contributions: Conceptualization, D.I.T., R.S.L. and N.B.R.B.; Data curation, D.I.T., N.B.R.B. and L.Q.H.; Formal analysis, D.I.T., N.B.R.B., J.O.S.L., M.O., L.Q.H. and E.B.C.; Funding acquisition, M.O. and M.Á.B.G.; Investigation, D.I.T., R.S.L., N.B.R.B., J.O.S.L., D.G.F., L.Q.H., R.E.T.M., E.B.C. and M.Á.B.G.; Methodology, D.I.T., R.S.L., N.B.R.B., J.O.S.L., D.G.F., L.Q.H. and R.E.T.M.; Project administration, R.S.L., M.O. and M.Á.B.G.; Resources, M.O. and M.Á.B.G.; Software, D.I.T., R.S.L., N.B.R.B., J.O.S.L., D.G.F., L.Q.H., R.E.T.M. and E.B.C.; Supervision, R.S.L. and M.O.; Validation, N.B.R.B.; Visualization, R.S.L., N.B.R.B., M.O. and M.Á.B.G.; Writing—original draft, D.I.T. and N.B.R.B.; Writing—review & editing, D.I.T., R.S.L., N.B.R.B., J.O.S.L., D.G.F., M.O., L.Q.H., R.E.T.M., E.B.C. and M.Á.B.G. All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by Public investment project GEOMATICA (SNIP N◦ 312235), executed by the Cloud Forest’s Sustainable Development Research Institute (INDES-CES) of the National University Toribio Rodríguez of Mendoza (UNTRM). Acknowledgments: The authors acknowledge and appreciate the support of the Research Institute for the Sustainable Development of the Jungle brow zone (INDES-CES) of the National University Toribio Rodríguez of Mendoza (UNTRM). They also express thanks to the group of experts who developed the matrices (R.O.G.R., H.A.C.H., L.G.B.R., J.V.T.E., J.A., L.N.Z.L., E.H., J.E.V.G., and L.M.G.R). Conﬂicts of Interest: The authors declare no conﬂict of interest. Agronomy 2020, 10, 1898 15 of 18

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