Harnessing drought tolerance in a reference set of Andean amaranths

Harnessing drought tolerance in a reference set of Andean amaranths | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Harnessing drought tolerance in a reference set of Andean amaranths View ORCID Profile Ramiro. N. Curti , View ORCID Profile Jonatan Rodriguez , View ORCID Profile Pablo Ortega-Baes , View ORCID Profile Sergio J. Bramardi , Berta Velásquez , Alberto J. Andrade doi: https://doi.org/10.1101/2025.08.06.668942 Ramiro. N. Curti 1 Laboratorio de Investigaciones Botánicas (LABIBO) – Consejo Nacional de Ciencia y Técnica (CONICET), and Sede Regional Sur Metán, Universidad Nacional de Salta , Salta, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ramiro. N. Curti For correspondence: rcurti{at}agro.uba.ar Jonatan Rodriguez 1 Laboratorio de Investigaciones Botánicas (LABIBO) – Consejo Nacional de Ciencia y Técnica (CONICET), and Sede Regional Sur Metán, Universidad Nacional de Salta , Salta, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonatan Rodriguez Pablo Ortega-Baes 1 Laboratorio de Investigaciones Botánicas (LABIBO) – Consejo Nacional de Ciencia y Técnica (CONICET), and Sede Regional Sur Metán, Universidad Nacional de Salta , Salta, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pablo Ortega-Baes Sergio J. Bramardi 2 Centro de Investigaciones en Toxicología Ambiental y Agrobiotecnología del Comahue (CITAAC-CONICET), Universidad Nacional del Comahue , Neuquén, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sergio J. Bramardi Berta Velásquez 3 Universidad Nacional de Jujuy , Jujuy, Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alberto J. Andrade 4 INBIAL (Instituto de Biología de la Altura), Universidad Nacional de Jujuy , Argentina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Climate change and low-input farming systems increasingly expose crops to drought stress. Andean amaranths ( Amaranthus spp .), as neglected and underutilized species, offer potential for climate-resilient agriculture due to their inherent drought tolerance and adaptability. In Northwest Argentina (NWA), a region with high environmental heterogeneity, exploiting this genetic diversity may improve food security. This study evaluated drought tolerance and yield stability among Andean amaranth genotypes to: assess the effects of genotype and G×E interaction, determine the potential for selecting specifically adapted genotypes, and identify high-yielding, stable genotypes for drought-prone conditions. Eleven genotypes (cultivars, breeding lines, and landraces of A . caudatus and A . mantegazzianus ) were tested across four agroecological zones in NWA under irrigated and drought-stressed conditions. Grain yield data were analyzed using linear mixed models and AMMI analysis. Genotypes differed significantly in grain yield across environments and irrigation regimes. Strong G×E interactions led to genotype re-ranking across sites. Several A . caudatus breeding lines (G1, G2, G3, G6) combined high yield and stability. The A . mantegazzianus landrace (G11) was highly stable but low yielding. Amaranth genotypes showed distinct responses to drought, with some lines exhibiting broad adaptation and others, specific adaptation to stress-prone environments. Introduction Many areas face critical food security challenges due to increasing agricultural demand and climate change’s impact on crop productivity ( Lobell and Di Tommaso 2025 ). Among these impacts, drought stress stands out as a major abiotic stressor ( Challinor et al . 2014 ). Significant advancements in breeding for crop yield in drought-prone regions have been achieved through extensive screening under abiotic stress ( Ceccarelli 1994 ; Bänziger and Cooper 2001 ; Xangsayasane et al . 2014 ). Key strategies for breeding drought tolerance in low-input farming systems involve effective stress management, direct selection for specific adaptation, and the utilization of locally adapted germplasm (landraces) ( Ceccarelli and Grando 1991 ; Bänzinger 2000; Ceccarelli et al . 2007 ). These principles suggest that selecting locally adapted germplasm can significantly improve breeding efficiency by exploiting genotype-by- environment (G×E) interactions ( Hatfield and Walthall 2015 ). Furthermore, developing new varieties through participatory plant breeding is likely to translate into their fast adoption in low-input environments ( Ceccarelli 2015 ). Sustained use of genetic variability in neglected and underutilized species (NUS) offers a key adaptation strategy to mitigate effects of climate change ( Padulosi et al . 2011 ). NUS are non-commodity crops marginalized for various reasons, yet they continuously evolve and adapt to environmental pressures ( Andreotti et al . 2022 ). This ongoing adaptation makes them a dynamic source of alleles vital for crop improvement ( Ulian et al . 2020 ). The Andean region, recognized as one of the world’s eight centers of domestication, boasts a high concentration of NUS ( Galluzzi and López Noriega 2014 ). Among the various NUS seed crops, the pseudocereal group includes species from the genera Chenopodium and Amaranthus (family Amaranthaceae). These species are notable for their high nutritional quality and remarkable adaptability to a wide array of abiotic stresses ( Joshi et al . 2018 ; Anuradha et al . 2023 ). Notably, amaranths exhibit drought resistance, requiring less water than many conventional staple crops (Pulvento et al . 2022; Gönen et al . 2024 ). This characteristic positions amaranths as a promising dryland crop for farmers in rainfed agricultural areas ( Espitia-Rangel 1994 ; Pulvento et al . 2015 ). The cultivated Andean amaranth grain includes the widely recognized species Amaranthus caudatus , alongside a taxonomically questioned entity known as A . mantegazzianus . First described by the Argentinean botanist Armando Hunziker in Los pseudocereales de la agricultura indígena de America , A . mantegazzianus was initially considered an endemic cultigen and the only domesticated species in Argentina. Its distribution is concentrated within the Calchaqui Valleys of Northwest Argentina, encompassing the provinces of Catamarca, Tucumán, and Salta (Hunziker 1952). Despite later treatments suggesting A . mantegazzianus may be synonymous with A . caudatus or a derivative of A . edulis ( Brenner et al . 2000 ; Alemayehu et al . 2015), its distinct panicle morphology sets it apart from other cultivated amaranths. Most cultivated amaranths exhibit a typical amaranthiform panicle, where glomeruli (flower clusters) are arranged along the secondary axis of the inflorescence. In contrast, A . mantegazzianus possesses a glomerulate panicle, characterized by glomeruli arranged on third-order axes. This unique panicle structure is also observed in quinoa ( Chenopodium quinoa ), another related pseudocereal originating from the Andean region ( Curti et al . 2012 ). For the purpose of this study, we maintain the species identity of A . mantegazzianus , as early cytological studies validated it specific status ( Greizerstein and Poggio 1994 ; Bonasora et al . 2013 ), and until further molecular studies evaluates its relationships within the Andean gene pool. Field evaluations of amaranth germplasm have been conducted across various Latin American countries through the Prueba Americana de Cultivares de Amaranto to identify promising cultivars for high and stable seed yield (Mujica 1997). While these multi-environmental trials (METs) revealed a high potential for amaranth production, significant variability in crop yield was observed across locations, along with cultivar-specific responses (Mujica 1997). This highlights a substantial genotype- by-environment (G×E) interaction, complicating the selection of broadly adapted cultivars. This challenge is particularly pertinent in the Andean agricultural system, recognized as an extremely complex environmental mosaic (Jacobsen et al . 2003). Here, diverse environmental factors, varying across toposequence and latitudinal range, profoundly influence crop yield and quality ( Curti et al . 2014 ). Furthermore, Andean farmers typically operate with minimal inputs, cultivating amaranths on marginal lands where drought stress often represents a primary limiting factor for production ( Kalinowski et al . 1992 ). Northwest Argentina (NWA) is a vast region characterized by a diverse array of environments ( Curti et al . 2012 ). To the west, the Cordillera Occidental, an extension of Bolivia’s Cordillera Real, introduces high-altitude landscapes known as the Puna. This ecoregion, while generally exhibiting consistently low year-round temperatures, is remarkably heterogeneous from north to south in terms of precipitation, temperature, and salinity ( Ferrero and Villalba 2019 ). The Puna’s extremely short frost-free period severely limits the growing season, necessitating the cultivation of hardy crops capable of tolerating cold, drought, and saline conditions ( Curti et al . 2012 ). Conversely, to the east, the Bolivian Cordillera Oriental extends into the region, creating high-altitude environments that are notably warmer than the Puna ( Curti et al . 2012 ). Situated between these two mountain ranges, a series of gorges stretch from north to south, forming fertile areas that offer greater agricultural potential ( Curti et al . 2012 ). During Prueba Americana de Cultivares de Amaranto , a test site in Purmamarca, Jujuy province, located in Northwest Argentina (NWA), demonstrated remarkable results. Despite a significant water deficit throughout the growing season, amaranth crops yielded acceptable grain (Mujica 1997). This observation suggests that the genetic variability in drought tolerance within Andean amaranth populations could be a valuable resource. This variability could be leveraged to develop new amaranth varieties specifically adapted to environments with varying water availability. Furthermore, given that NWA has experienced long-term shifts in precipitation patterns, characterized by changes in both frequency and amount ( Ferrero and Villalba 2019 ), the ongoing search for amaranth germplasm with desirable levels of drought tolerance is highly encouraged. This study aims to screen genotypic variability for drought stress tolerance among Andean amaranth cultivars, breeding lines, and landraces within managed stress environments. We hypothesize that by managing drought stress through irrigation differences, we will observe the largest genotypic variation, and productivity will be maximized by selecting genotypes for specific environments. Specifically, we addressed the following objectives: i) to examine the relative magnitude of genotype (G) and genotype by environment (G×E) interaction effects both between and within Andean amaranth species; ii) to evaluate the relative advantage of selecting for specific adaptation by exploiting G×E interaction; and iii) to identify high-yielding and stable Andean amaranth genotypes suitable for drought-prone environments. Material and Methods Genotypes and testing locations A reference set of eleven amaranth genotypes, comprising six breeding lines, one landrace and four cultivars of Amaranthus caudatus and one landrace of A . mantegazzianus ( Table 1 ), were evaluated in four sites in the province of Jujuy (Argentina). Genotype selection was based on diversity of origin and most came from the Prueba Americana de Cultivares de Amaranto , an international METs in which a set of genetically diverse cultivars was tested in several countries of Latin America (Mujica 1997). The genotypes originated from different breeding programs ( Table 1 ), and a short description of their pedigree follows. Breeding lines 10c, 294ab, 32a, 38a, 44a and G403 and cultivar Noel Vietmayer were selected from ecotypes collected in Tarija (Bolivia) at Centro de Investigaciones en Cultivos Andinos (CICA), Cuzco (Peru) ( Kalinowski et al . 1992 ). Cultivar INIAP-alegría was selected from a local landrace at Instituto Nacional de Investigaciones Agropecuarias (INIAP, Quito, Ecuador) for cultivation at mid-elevation valleys (Nieto 1993). Cultivar UTAB- Cahuayuma was selected from a local landrace at Estación Experimental Pairumani, Instituto Boliviano de Tecnología Agropecuaria (IBTA; currently PROINPA) ( Rojas et al . 2010 ). The CVR genotype derived from mass selection of a local landrace collected in Jujuy (Argentina), whereas genotype MTGZ correspond to another amaranth seed crop ( A . mantegazzianus ) which was selected from a local landrace collected in Jujuy (Argentina) ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Code, species, origin and status of the 11 genotypes of Andean amaranths evaluated in multi-environment trials in Northwest Argentina. The experimental sites were located in farmer’s fields including the major agroecological zones in which amaranth crops grown in the province of Jujuy. Accordingly, the sites represent the Target Population of Environments (TPEs) into which the amaranth breeding program operates. The León site (Department of Manuel Belgrano), located at low elevation (1754 m asl) represents a typical humid valley environment. Purmamarca (Department of Tumbaya) and Hornaditas (Department of Humahuaca) sites represents a typical dry valley environment. Purmamarca is located at a higher elevation than León (2324 m asl) but at lower than Hornaditas (3230 m asl). The last site selected, La Quiaca (Department of Cochinoca), is located at high elevation (3440 m asl) and represents a typical Puna environment. Precipitation and evapotranspiration values for the entire growing season were obtained from meteorological stations located at 500 m distance from the experimental sites. Field trials Two trials were planted side by side at each experimental site at a safe distance of 40 to 50 m. Between trials, corn ( Zea mays L.) was sown to avoid the neighbor effect of the treated trial on the untreated trial. In each of the two trials, all genotypes were planted in the field in a randomized complete block design with four replicates. The first trial served as a control and received deep irrigation before planting and then every 10 or 15 days following local practices (furrow irrigation) to avoid water deficits during the crop cycle. The second trial was drought stressed by stopping irrigation between the 85 and 125 days after crop emergence at the stage of anthesis (i.e., when at least one flower is open, corresponding to stage R4 in the phenological scale of Henderson et al . (1998) . After the stage of the end of anthesis the irrigation was reinitiated in the drought stress trial in the same fashion as the control trial. Plots were three rows with 70 cm inter-row spacing and 5 m long. Sowing density was 47,620 grains ha -1 and weeds were removed by hand and fungicides and insecticides applied upon diseases and pest detection in the field. The experiments were fertilized at a rate of 43 kg N ha -1 after crop emergence. At physiological maturity (visually determined by the change in panicle color, corresponding to stage R7 in the phenological scale of Henderson et al . (1998) ), ten contiguous plants from each replicate were harvested to record grain yield adjusted to 14% of moisture content and expressed in tones ha -1 . Statistical analysis A linear mixed model was set up for an analysis of grain yield across trials and sites as follow: with y designating the grain yield of the j- th genotype in i- th site, k- th trial and l- th block, 𝜇 is the grand mean, S is the effect of i- th site, G is the effect of j- th genotypes, T is the effect of k- th trial (drought vs. irrigation), GS ij is the interaction effect of j -th genotype and i -th site, ST ik is the interaction effect of i -th site and k -th trial, GT jk is the interaction effect of the j -th genotype and k -th trial, GST ijk is the triple interaction effect of genotype j -th and i -th site and k -th trial, B the random effect of l -th block nested within the i -th site by k -th trial, and 𝜀 the random error. In the model, the genotypes, trials and sites were considered fixed whereas blocks were considered random. The analysis was computed using the PROC GLM in SAS® software (SAS Institute 2004). In a second-stage, we restructured the G×S×T dataset into a two-way G×E dataset, treating each unique combination of location and trial as a distinct environment. We then performed an Additive Main and Multiplicative Interaction (AMMI) analysis using the metan package ( Olivoto and Lúcio 2020 ) within the R statistical environment ( R core team, 2024 ). This restructuring was necessary because while the ANOVA component of AMMI can handle factorial designs of any complexity, the principal components analysis (PCA) part of the AMMI model specifically requires a two-way dataset (Paderewski et al . 2016). The aim was to model the genotype-by-environment interaction to describe the pattern of genotypic response and identify high yielding and stable genotypes. The number of IPCA (Interaction Principal Components Analysis) axes to retain in the AMMI model was based on the Gollob’s test. The Biplots were constructed for the AMMI1 and AMMI2 models. We computed the Weighted Average of Absolute Scores (WAAS, Olivoto et al . (2019) ) to determine the stability of genotypes and the discriminant ability of sites. Finally, with the computed WAASBY index to get insight into the simultaneous selection for mean performance and stability ( Olivoto et al . 2019 ). Results In the León site, precipitation exceeds evaporation for the majority of the growing season, resulting in negligible water deficit, with the exception of the late flowering stage ( Fig. 1a ). In contrast, the Purmamarca and Hornaditas sites exhibit evaporation values exceeding precipitation throughout the entire growing season, leading to substantial water deficits, particularly during the flowering period ( Fig. 1b and c ). At La Quiaca, precipitation exceeds evaporation only at the start of the growing season, while a high-water deficit was observed during the flowering period as evaporation increases towards the season’s end ( Fig. 1d ). Download figure Open in new tab Fig. 1 Mean rates of precipitation (P) and evaporation (E) and the difference between them (P-E) (mm water day -1 ) for the four sites in which Andean amaranths were evaluated: (a) León; (b) Purmamarca; (c) Hornaditas and (d) La Quiaca. The sowing time (S) and the flowering period (FL) are shown. A statistically significant genotype-by-location-by-trial interaction effect was observed for grain yield among the Andean amaranth genotypes evaluated in Northwest Argentina ( Table 2 ). The performance plots illustrated a consistent re- ranking of genotypes across different sites between irrigated and drought-stress trials, indicating strong crossover interactions (Figure S1). The Additive Main effects and Multiplicative Interaction (AMMI) model corroborated the G×E interaction effect and revealed that the first five Interaction Principal Component Axes (IPCA) were significant in explaining the variation in genotype performance across environments (Table S2). View this table: View inline View popup Download powerpoint Table 2. The ANOVA for genotype × site × trial adjusted means of Andean amaranth grain yield. In the AMMI1 model, environments E4 and E6 were positioned on opposing sides along the IPCA axis, demonstrating a pronounced influence on their capacity to differentiate among genotypes (Fig. 3a). The remaining environments exhibited short vector lengths, implying a limited discriminatory power along the first IPCA. Genotypes G2, G3, and G11 were located near the origin of the IPCA axis, suggesting their yield stability across all evaluated environments (Fig. 3a). Notably, G2 displayed the highest grain yield values, while G11 exhibited the lowest. Genotypes G1, G4, G5, G6, G7, G8, and G9 demonstrated cross-over interaction between E4 and E6, indicating specific adaptation to these environments ( Fig. 2a ). Genotypes G1, G5, and G10 showed high grain yield than grand mean, whereas G9 showed low grain yield, however, all exhibited improved performance in environment E4 ( Fig. 2a ). Conversely, genotypes G6 and G7, which had high yields, and G4 and G8, which had low yields, showed improved performance in environment E6 ( Fig. 2a ). Download figure Open in new tab Fig. 2 Additive Main effects and Multiplicative Interaction (AMMI) model for amaranth grain yield and stability analysis. AMMI for the: (a) first IPCA plotted against grain yield (AMMI1) and, (b) the first two IPCA2 (AMMI2); (c) stability analysis based on Weighted Average of Absolute Scores against grain yield; and (d) ranking of genotypes based on WAASBY index and the opportunity to simultaneous selection for mean performance and stability. Genotypes (G#) are denoted by symbols (circle) and Environments (E#) by vectors, E1: León irrigated; E2: León stressed; E3: Purmamarca irrigated; E4: Purmamarca stressed; E5: Hornaditas irrigated; E6: Hornaditas stressed; E7: La Quiaca irrigated; E8: La Quiaca stressed. In the AMMI2 model, the environments spanned the entire Euclidean space, underscoring the substantial G×E interaction effects. Consistent with the AMMI1 model, environments E4 and E6 were situated on opposing sides along the IPCA1 axis, demonstrating the same contrasting influences ( Fig. 2b ). Furthermore, the IPCA2 axis revealed contrasting effects for environments E8 and E7. Environments E3 and E2 exhibited a close relationship with E7, as indicated by angles less than 90° ( Fig. 2b ). Conversely, E1 and E5 displayed short vectors, suggesting that their capacity to capture G×E interactions would likely be accounted for by higher-order IPCAs ( Fig. 2b ). Genotypes G1, G5, and G9 showed improved performance in environment E4, while G6, G7, and G8 performed better in E6. Conversely, G4 and G11 exhibited improved performance towards E2, E3, and E7, whereas G2 and G10 showed improved performance towards E8 ( Fig. 2b ). Notably, G3 was the only genotype exhibiting stability, as it was positioned at the origin of both the IPCA1 and IPCA2 axes ( Fig. 2b ). The biplot of Weighted Average of Absolute Scores (WAASB) values plotted against mean grain yield facilitates a four-quadrant classification system for the concurrent interpretation of yield performance and stability ( Fig. 2c ). Genotypes G4, G8, and G9, along with environments E2, E4, and E8, situated in quadrant I, are characterized as unstable genotypes or environments exhibiting high discriminatory power but with productivity below the overall mean ( Fig. 2c ). Similarly, genotypes G5, G7, and G10, and environments E6 and E7, located in quadrant II, also demonstrate instability and high discrimination ability, yet their productivity surpasses the grand mean ( Fig. 2c ). Genotype G11, positioned in quadrant III, displays low productivity but is considered stable due to its lower WAASB values ( Fig. 2c ). Conversely, genotypes G1, G2, G3, and G6, and environments E1, E3, and E5, found in quadrant IV, are identified as highly productive and broadly adapted, owing to their high mean yield and superior stability performance (lower WAASB values) ( Fig. 2c ). The genotype ranking considering both the stability (WAAS) and mean performance based on the WAASBY index confirmed the high productive and broadly adaptation of genotypes G1, G2, G3 and G6 ( Fig. 2d ). Discussion We applied managed drought stress through irrigation at the flowering stage and found that it differentially affected the performance of Andean amaranth genotypes across sites. This revealed clear genotypic variation in drought tolerance. Sensitivity to stress also varied with the climatic conditions at each site. Most genotype-by-environment (G×E) interactions resulted in a reranking of genotype performance, complicating the development of effective selection strategies. Therefore, improving grain yield in Andean amaranths requires breeding programs to explicitly address the G×E interactions observed within Target Population of Environments (TPEs) from Northwest Argentina. Drought sensitivity around flowering in Andean amaranths is comparable to other grain or vegetable amaranth species cultivated in North America and Asia ( Johnson and Henderson 2002 ; Jamalluddin et al . 2021 ). Furthermore, yield reductions under managed stress highlight that flowering stage as critical for yield determination ( Gönen et al . 2024 ). The study also found performance differences between species, especially within A . caudatus , which showed significant genetic variability in drought response. Future studies in amaranth research should focus on understanding the morpho-physiological mechanisms that explain varying sensitivities to drought stress within this species. Prior research on other Amaranthus species offers valuable insights. For instance, A . cruentus and A . hypochondriacus have demonstrated enhanced root exploration under drought conditions, leading to improved water use efficiency ( Johnson and Henderson 2002 ). In contrast, A . hybridus and other vegetable amaranth varieties employ a different strategy, reducing stomatal conductance as a water conservation mechanism when exposed to drought and salinity stresses ( Liu and Stützel 2002 , 2004 ; Omamt et al . 2006; Jamalluddin et al . 2019). Therefore, future field experiments should prioritize investigating root exploration in Andean amaranth species under drought stress. This research will be crucial for developing more drought-resilient amaranth varieties, which is vital for food security in regions prone to water scarcity. Our findings reveal that A . caudatus genotypes at different breeding stages exhibit distinct performance patterns, indicating varied adaptive strategies across the TPEs. The first Interaction Principal Component Axis (IPCA1) of the AMMI1 model demonstrated that G2 and G3, both A . caudatus breeding lines ( Table 1 ), displayed contrasting responses. G3 showed high stability across environments, while G2 exhibited higher grain yield ( Fig. 2a ). Conversely, G11, a landrace of another amaranth species ( Table 1 ), was positioned near the origin, signifying consistent performance across environments but lower grain yield. This aligns with previous research indicating that A . mantegazzianus can achieve acceptable yields in Northwest Argentina with approximately 176 mm of accumulated rainfall (Mujica 1997). The current study’s environments had rainfall ranging from 111 to 190 mm ( Fig. 1b and c ), further supporting the adaptability of A . mantegazzianus under such conditions. The AMMI2 biplot confirmed the high stability of breeding line G3. In general, the A . caudatus cultivars displayed close adaptation to specific environments along the first IPCA1. This is exemplified by the contrasting responses of G8, G9, and G10 between E4 and E6 ( Fig. 2b ), which were the most drought-stressed environments evaluated ( Fig. 1b and c ). A similar performance pattern was observed for other A . caudatus breeding lines (G1, G5, and G6) and the landrace G7 ( Fig. 2b ). On the other hand, G2 and G11 exhibited divergent environmental responses along the second IPCA. Specifically, G2 demonstrated improved performance in environments E1, E5, and E8, whereas G11 in E2, E3, and E7 ( Fig. 2b ). These environmental groupings appear geographically and climatically disparate, as they varied in altitude, accumulated precipitation, and management practices. Consequently, despite observing strong crossover G×E interactions, the results do not support their subdivision into distinct mega-environments. These findings diverge from those reported for quinoa, in which evaluations conducted in analogous environments revealed that the delineation into mega-environments remained consistent, irrespective of alterations in management practices ( Curti et al . 2014 ; Agüero et al . 2023 ). Our results reveal a clear distinction between environments based on their discriminatory capacity and average grain yield. Environments characterized by high discriminatory capacity but lower average yields were predominantly drought-stressed (E2, E4, and E8). Conversely, irrigated environments (E6 and E8) exhibited higher average grain yields. All evaluated cultivars, the landrace, and some breeding lines of A . caudatus were associated with these high-yielding, irrigated environments, yet they displayed greater instability. Notably, no environment fell into Quadrant III (low discriminatory capacity and low grain yield) in our analysis ( Fig. 2c ). Interestingly, G11 demonstrated superior stability ( Fig. 2c ). Conversely, irrigated environments (E1, E3, and E5) showed low discriminatory capacity but higher-than-average grain yields. The remaining A . caudatus breeding lines were associated with these stable environments, exhibiting consistent performance across the target population of environments (TPEs). A comprehensive evaluation of genotypes based on their average grain yield and stability clearly indicated the higher stability of A . caudatus breeding lines and the A . mantegazzianus landrace, while A . caudatus cultivars demonstrated specific adaptation ( Fig. 2d ). Future research should explore the potential of exploiting genotype- environment (G×E) interactions for specific adaptation within the Northwest Argentina TPEs. This could involve focusing on the observed ranking changes in A . caudatus cultivars and its landrace, and, to some extent, selecting breeding lines that exhibit crossover interactions. Alternatively, a breeding strategy that minimizes G×E interaction and prioritizes the selection of genotypes for wide adaptation and high stability remains a viable option. Subsequent studies should assess the logistical and resource demands of implementing each of these breeding strategies within these complex TPEs. Declaration of Funding This research did not receive any specific funding. Data Availability Statement The data that support this study will be shared upon reasonable request to the corresponding author. Conflicts of Interest The authors have no relevant financial or non-financial interests to disclose. Acknowledgments We would like to extend our thanks to all the farmers involved in the evaluation of amaranth materials. References ↵ Agüero JJ , Acreche MM , Sühring SS , Bertero HD , Curti RN ( 2023 ) Genotype- dependent responses of Andean and Coastal quinoa to plant population density for yield and its physiological determinants in Northwest Argentina . Crop & Pasture Science 75 , CP23040. doi: 10.1071/CP23040 OpenUrl CrossRef Alemayehu FR , Bendevis MA , Jacobsen S -E. ( 2015 ) The potential for utilizing the seed crop amaranth ( Amaranthus spp.) in East Africa as an alternative crop to support food Security and climate change mitigation . 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