Examinando por Autor "Pizarro Carcausto, Samuel Edwin"

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Agronomic variables outperform multispectral indices for individual plant yield prediction in Andean quinoa
(Elsevier B.V., 2026-04-18) Pizarro Carcausto, Samuel Edwin; García Seguil, Erika Janina; Gavino Lulo, Esthefany Irene; Requena Rojas, Edilson Jimmy; Ortega Quispe, Kevin Abner; Cccopi Trucios, Dennis
Accurate pre-harvest yield estimation is essential for decision-making in high-altitude agriculture. This study evaluated agronomic and multispectral UAV variables for near-harvest prediction of individual quinoa grain weight, with data collected across six phenological stages to identify when predictors achieve reliable performance, under Andean conditions. A total of 374 plants were monitored across six phenological stages at Santa Ana Experimental Station (Huancayo, Peru, 3280 m a.s.l.) during 2024. OLS, Random Forest, Support Vector Machine, and Neural Network models were trained using agronomic-only (AGRO), spectral-only (IND), and combined (COMP) predictor sets, evaluated through 5-fold cross-validation reporting mean ± standard deviation. Agronomic and combined models achieved moderate performance (R² = 0.22–0.25, RPD = 1.10–1.15), suitable for relative plant ranking in breeding programs, while spectral-only models failed across all algorithms (R² ≤ 0.044, CCC ≤ 0.080), constrained by saturation, phenological decoupling, and canopy heterogeneity. Variable importance analysis confirmed that late-season structural traits dominated predictions, while spectral indices contributed marginally despite including red-edge bands. These results challenge spectral-only approaches for individual plant phenotyping in heterogeneous canopies, demonstrating that integrating simple ground measurements with UAV spectral data is essential for reliable quinoa yield estimation.
Assessment of soil fertility variability for maize production in highland agroecosystems of Peru
(Polish Society of Ecological Engineering (PTIE), 2026-04-01) Garcia Seguil, Erika Janina; Ccopi Trucios, Dennis; Requena Rojas, Edilson Jimmy; Sanabria Quispe, Samuel; Arias Arredondo, Alberto Gilmer; Gavino Lulo, Esthefany Irene; Azabache, Andres; Pizarro Carcausto, Samuel Edwin
Maize (Zea mays L.) is central to food, feed, and rural livelihoods, yet the yields in Peru’s highlands remain modest, underscoring the need for spatially explicit soil diagnostics. This study aimed to characterize the spatial variability of soil fertility in a highland maize production area of the southern Mantaro Valley and translate those patterns into site-specific management zones. The authors sampled the arable layer (0–30 cm) at 100 plots and analyzed pH, electrical conductivity, exchangeable acidity, texture, organic matter (OM), total nitrogen (N), available phosphorus (P), available potassium (K), exchangeable cations (Ca, Mg, Na, K), and calcium carbonate (CaCO₃). Laboratory data were integrated with environmental covariates using geostatistics, Random Forest, and GIS to generate high-resolution maps. Results showed uneven distributions in key attributes about 25% of the area with P deficiency, 15% with localized K shortages, and ~20% with OM < 2% while pH and CEC were comparatively stable. Random Forest achieved strong predictive performance for relatively stable properties (e.g., OM, pH, exchangeable cations), whereas mobile nutrients (available P, exchangeable K) were less predictable. The resulting products constitute the first high-resolution soil-fertility baseline for maize in the southern Mantaro Valley. The maps delineate fertilization management zones and provide a practical basis for preliminary rate recommendations that target constraints while avoiding surpluses. Future work will refine these zoned recommendations through yield-response trials, seasonal monitoring of mobile nutrients, and farmercentered decision-support tools, with the goal of improving nutrient-use efficiency, sustaining maize productivity, and reducing environmental risks across the valley.
Bioaccumulation of heavy metals in high Andean crops of the Peruvian Andes: comparative evaluation between irrigated and dry systems
(Elsevier B.V., 2025-12-13) Ccopi Trucios, Dennis; Requena Rojas, Edilson Jimmy; Ortega Quispe, Kevin Abner; Solórzano Acosta, Richard Andi; Révolo Acevedo, Ronald; Pizarro Carcausto, Samuel Edwin
Heavy metal contamination in Andean agricultural systems is a growing concern for food safety and environmental health. This study assessed the concentrations and bioaccumulation patterns of eleven metals (Cd, Pb, As, Cr, Ni, Cu, Zn, Mn, Fe, Hg, Mo) in seven representative crops cultivated under irrigated and rainfed conditions in the Mantaro Valley, central Peruvian Andes. Soil and foliar samples were collected from paired plots, and bioaccumulation factors (BAF) were calculated to evaluate metal transfer to plant tissues. Irrigated soils showed higher and more homogeneous concentrations of Cd, Pb, and As, reflecting long-term accumulation from historical mining activities and irrigation with contaminated water from the Mantaro River. Foliar concentrations exceeded Codex Alimentarius limits for Cd, Pb, and As in several crops, especially potato and broad bean. BAF analyses revealed distinct crop-specific behaviors: potato, quinoa, and broad bean frequently exhibited BAF >1 for metals such as Cd, Cu, Zn, and Mn, indicating active uptake and translocation. In contrast, cereals such as maize and barley maintained low BAF values (<1), suggesting conservative absorption patterns. Irrigation increased the bioavailability of several metals, resulting in higher foliar concentrations and elevated BAF values compared to rainfed systems. Multivariate analyses further differentiated metal accumulation profiles by crop type and water management system. These findings highlight the need for strengthened monitoring of high-accumulation crops and improved soil and water quality management in historically contaminated Andean agricultural regions.
Composition, diversity, and value of ecological importance in Andean grassland ecosystems according to the altitudinal gradient in the Huacracocha micro-watershed, Peru
(Sciencedomain International, 2023-08-12) Yaranga Cano, Raul Marino; Pizarro Carcausto, Samuel Edwin; Cano, Deyvis; Chanamé, Fernan C.; Orellana, Javier A.
Aims: determine the composition and floristic diversity, the similarity between sites based on the distribution of species in the altitudinal gradient, and determine the value of ecological importance, in Andean grassland ecosystems. Study Design: Original research. Place and Duration of Study: This study took place in the Huacracocha micro-watershed in the Central Highlands of Peru, during the rainy season (January - March 2022). Methodology: The agrostological evaluation points were determined taking into account twelve sites of interest were determined, located from the lowest part of the micro-watershed (4091.8 masl) to the part with the highest vegetation cover (4512.27 masl), the agrostological reading process at each evaluation site was carried out using the radial transect method with the line and intercept point technique. Results: We observed the presence of the presence of 78 vascular species, included in 51 genus and 21 families, was found. The dominance of certain species characterized the type of grassland vegetation, and at least 3 species determined the similarity between sites. The alpha diversity index was low, and the value of ecological importance ranged between 0.0062 and 0.2194. Conclusion: It was concluded that the Andean grassland ecosystems are constituted by a complex community of grasslands based on numerous floristic families, genus, and species, likewise, the dominance of species among the shared sites characterizes the vegetation type, and the diversity index and the IVI determine the complex structural characteristics with great biodiversity.
Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration
(Elsevier, 2024-12-12) Pizarro Carcausto, Samuel Edwin; Pricope , Narcisa G.; Vera Vilchez, Jesús Emilio; Cruz Luis, Juancarlos Alejandro; Lastra Paucar, Sphyros Roomel; Solórzano Acosta, Richard Andi; Verástegui Martínez, Patricia
The quality and safety of soil are crucial for ensuring social and economic development and providing contaminant-free food. The availability and quality of soil data, particularly for multiple metals and metalloids, are often insufficient for comprehensive analysis. Soil formation and the distribution of metals are shaped by various factors such as geology, climate, topography, and human activities, making accurate modeling highly challenging. Additionally, agricultural intensification, urban expansion, road construction, and mining activities frequently result in soil pollution, posing serious risks to ecosystems and human health. This study aims to integrate diverse geospatial datasets with machine learning for high resolution soil contamination mapping (10 m spatial resolution) in a major agricultural region of Peruvian highlands. This study mapped 25 elements (Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn, V) in the Peruvian Mantaro Valley using a training dataset of 109 topsoil samples combined with various geospatial datasets (remote sensing, climate, topography, soil data, and distance). The model provided satisfactory results in predicting the spatial distribution of the selected elements, with R² values ranging from 0.6 to 0.9 for most elements. Edaphic, climate, and topographic covariates were the most significant predictors, particularly for croplands near rivers, whereas spectral variables were less important. The results reveal As, Pb, and Cd concentrations significantly above permissible limits, highlighting urgent health risks. These findings suggest that it is feasible to identify polluted soils and improve regulations based on widely available geospatial datasets with minimal training data. The study contributes to the development of models to assess the impact of pollutants on environmental and human health in the short-to-medium term, emphasizing the need for further research on the translocation of toxic metals into food crops and the implications for public health.
Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation
(Elsevier, 2025-08-27) Pizarro Carcausto, Samuel Edwin; Requena Rojas, Edilson Jimmy; Barboza, Elgar; Peña Elme, Eunice Dorcas; Arias Arredondo, Alberto Gilmer; Ccopi Trucios, Dennis
The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial distribution, ecological risk, and human health implications of 14 heavy metals, metalloids, and trace elements in surface soils surrounding the lake. Using 211 soil samples, we integrated remote sensing, land cover classification, and Random Forest machine learning models with spectral, edaphic, topographic, and proximity-based environmental covariates to predict contamination patterns and assess risk. Results reveal extreme contamination, with arsenic (As), lead (Pb), cadmium (Cd), and zinc (Zn) concentrations exceeding ecological thresholds by over 100-fold in agricultural zones. Ecological risk assessments using contamination degree (mCD), pollution load index (PLI), and risk index (RI) indicated that over 99 % of the study area exhibits very high to ultra-high contamination levels. Human health risk analysis identified unacceptable carcinogenic risks from As, Pb, and Cr across adult and pediatric populations, with arsenic presenting the greatest concern. The integration of geospatial tools and machine learning enabled precise identification of contamination hotspots and vulnerable land cover types, demonstrating the value of AI approaches for monitoring contaminated territories. These findings underscore the urgent need for coordinated environmental management, targeted remediation strategies, and community-based monitoring to protect public health and preserve Andean ecosystem integrity.
Ecological and Human Health Risk Assessment of Heavy Metals in Mining-Affected River Sediments in the Peruvian Central Highlands
(MDPI, 2025-09-16) Custodio, María; Pizarro Carcausto, Samuel Edwin; Huarcaya, Javier; Ortega Quispe, Kevin Abner; Ccopi Trucios, Dennis
Heavy metal contamination in rivers is a serious environmental and public health concern, especially in areas affected by mining. This study evaluated the levels of contamination and the associated ecological and carcinogenic risks in the sediments of the Cunas River, located in the central highlands of Peru. Sediment samples were collected from upstream and downstream sections. Several metals and metalloids were analyzed, including copper (Cu), chromium (Cr), iron (Fe), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), vanadium (V), zinc (Zn), antimony (Sb), arsenic (As), and cadmium (Cd). The ecological risk assessment focused on ten of these elements, while carcinogenic and non-carcinogenic risks were assessed for seven metals selected based on their toxicological importance. The results showed that Cd and Pb concentrations were higher in the downstream section. Cd and As exceeded ecological risk thresholds. Regarding human health, As and Pb surpassed the acceptable limits for both the Hazard Index (HI) and the Potential Carcinogenic Risk (PCR). According to EPA guidelines, these values indicate a potentially significant lifetime cancer risk. The main exposure routes include direct contact with sediments and the consumption of aquatic organisms. Continuous monitoring, phytoremediation actions, and restrictions on the use of contaminated water are strongly recommended to reduce ecological and health risks.
Ecological risk associated with potentially toxic elements in agricultural soils across coastal and highland valleys
(Elsevier Ltd., 2026-05-06) Pérez Porras, Wendy Elizabeth; Ccopi Trucios, Dennis; Flores Marquez, Ricardo; Carbajal Llosa, Carlos Miguel; Pizarro Carcausto, Samuel Edwin
Soil elemental composition in heterogeneous agroecosystems is shaped by interacting environmental and anthropogenic controls. This study evaluated the spatial variability of potentially toxic elements (PTEs: Cu, Cr, Fe, Mn, Mo, Ni, Pb, V, Zn, As, and Cd) across coastal (Chancay and Pativilca) and highland (Mantaro and Tarma) agricultural valleys of Peru. A stratified sampling design was combined with multivariate analyses (PCA, PERMANOVA, PERMDISP, and variance partitioning) and ecological risk assessment using integrated indices (PLI, mCd, SRI, and Nemerow index). The first two principal components explained 50.2% of total variance (PC1 = 36.8%; PC2 = 13.4%), reflecting distinct soil–geochemical and climatic–spatial gradients. The Valley × Zone interaction significantly structured elemental composition (R² = 0.049, p = 0.011), whereas crop type showed no significant effect (p = 0.838). Variance partitioning indicated that soil physicochemical, climatic, and spatial/topographic predictors jointly explained 60% of total variation (adjusted R² = 0.597), with the three-way shared fraction accounting for 28%, highlighting strong coupling among pedogenic, climatic, and topographic drivers. Ecological risk indices revealed clear spatial differentiation between systems. Highland valleys exhibited greater contamination intensity, spatial heterogeneity, and more frequent high-risk categories according to PLI, mCd, and SRI. In contrast, coastal valleys showed more homogeneous and diffuse accumulation patterns associated with long-term agricultural intensification. These findings underscore the need for regionally adapted soil monitoring frameworks that incorporate environmental gradients in the assessment and management of PTE-related ecological risk in agricultural landscapes.
Effects of Glomus iranicum inoculation on growth and nutrient uptake in potatoes associated with broad beans under greenhouse conditions
(MDPI, 2025-07-21) Contreras Pino, Douglas Lenin; Pizarro Carcausto, Samuel Edwin; Verastequi Martínez, Patricia; Solórzano Acosta, Richard Andi; Requena Rojas, Edilson Jimmy
The rising global demand for food, including potatoes, necessitates increased crop production. To achieve higher yields, farmers frequently depend on regular applications of nitrogen and phosphate fertilizers. As people seek more environmentally friendly alternatives, biofertilizers are gaining popularity as a potential replacement for synthetic fertilizers. This study aimed to determine how Glomus iranicum affects the growth of potatoes (Solanum tuberosum L.) and the nutritional value of potato tubers when grown alongside broad beans (Vicia faba L.). An experiment was conducted using potatoes tested at five dosage levels of G. iranicum, ranging from 0 to 4 g, to see its impact on the plants and soil. Inoculation with G. iranicum produced variable results in associated potato and bean crops, with significant effects on some variables. In particular, inoculation with 3 g of G. iranicum produced an increase in plant height (24%), leaf dry weight (90%), and tuber dry weight (57%) of potatoes. Similarly, 4 g of G. iranicum produced an increase in the foliar fresh weight (115%), root length (124%), root fresh weight (159%), and root dry weight (243%) of broad beans compared to no inoculation. These findings suggest that G. iranicum could be a helpful biological tool in Andean crops to improve the productivity of potatoes associated with broad beans. This could potentially reduce the need for chemical fertilizers in these crops.
Ensemble machine learning for digital mapping of soil pH and electrical conductivity in the Andean agroecosystem of Peru
(Frontiers Media S.A., 2025-11-06) Carbajal Llosa, Carlos Miguel; Barja , Antony; Pizarro Carcausto, Samuel Edwin
In agricultural systems, soil pH and electrical conductivity (EC) are crucial chemical properties that directly affect nutrient availability and microbial activity, but the challenging environment of the Peruvian Andes has limited research on their estimation. This study aimed to develop an ensemble learning method to predict soil pH and EC in Andean agroecosystems using environmental predictors. By using simple and weighted averaging, we developed a heterogeneous ensemble learning approach that integrates machine learning (ML) algorithms, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The weighted ensemble assigns weights to models based on their predictive accuracy, measured by R² from spatial cross-validation. Spatial patterns are noticeable, and pH displays greater spatial clustering than EC. Elevation was the most important predictor in ML models for both parameters. Ensemble models significantly outperformed individual models, with the weighted ensemble achieving R² >0.93 and reducing RMSE by approximately 72%. Among standalone models, RF and XGBoost performed best for pH, while SVM performed the best for EC. ANN models were the least effective. Uncertainty analysis indicated high confidence in pH predictions but moderate to high uncertainty in EC predictions, suggesting that EC is more challenging to predict. Ensemble models with optimized weighting provide robust and accurate mapping of spatially autocorrelated soil properties. The high-confidence pH maps are reliable for soil management decisions, while EC predictions, though more uncertain, effectively identify priority areas for future sampling and investigation.
Environmental, economic and social perceptions of community members on the role of water, soil and natural grasslands as a basis for local development in Acopalca, Peru
(Head Start Network for Education and Research, 2024-06-19) Maldonado Oré, Edith M.; Yaranga Cano, Raul Marino; Pizarro Carcausto, Samuel Edwin; Cano, Deyvis
The concept of ecosystem services has gained popularity among academics, researchers and policymakers to support environmental management and biodiversity conservation, so that many development projects in rural areas have merited investment for restoration and improvement of grassland ecosystems accompanied by training programs for the beneficiaries, With this criterion in mind, the study investigated the perception of puna pastoralists in Acopalca, Peru, regarding the degree of knowledge about the significance of the ecosystem services provided by soil-water-grasslands, with the objective of characterizing the environmental, social and economic dimensions of this local perception, through the application of a survey to the representative of the livestock family affiliated to two producers' associations. It was evidenced that cattle-raising families have a limited understanding of the role of the natural resources they directly access and little clarity on the relationship between natural pastures, family income and access to basic services. The results revealed limitations in environmental perception, evidencing a lack of knowledge about the multifaceted contribution of pastures. Social perception showed neutrality in the relationship between pastures and family income, and a discrepancy in access to basic services. The comparison between associations highlighted significant differences, indicating the need for training strategies adapted to the local idiosyncrasies of the beneficiaries. In conclusion, addressing the deficiencies identified in community understanding was essential to strengthening sustainable natural resource management in Acopalca. It highlights the importance of designing specific training programs, considering the particularities of each group, to promote self-management and community participation and thus achieve more comprehensive and sustainable local development.
Evaluation of nutrient extraction and uptake by forage grasses under high Andean mountain conditions in Peru
(Asian Journal of Agriculture and Biology (AJAB), 2026-03-20) Arias Arredondo, Alberto; Lopez Rodríguez, Melina; Cruz Luis, Juancarlos Alejandro; Requena Rojas, Edilson Jimmy; Ccopi Trucios, Dennis; Pizarro Carcausto, Samuel Edwin; Solórzano Acosta, Richard Andi
This study evaluated nutrient extraction and uptake in native forage grasses (Festuca dolichophylla and Calamagrostis chrysantha) and improved species (Lolium perenne and Dactylis glomerata) at 4,100 m a.s.l. in the Peruvian Andes using a completely randomized design. Results revealed significant interspecific variability in nutrient accumulation. Dactylis glomerata showed superior macronutrient accumulation, particularly Mg, while Lolium perenne achieved highest K extraction (0.07 t ha⁻¹) and biomass production. Native species demonstrated lower nutritional demands: Festuca dolichophylla reached maximum dry matter production (6 t ha⁻¹), while Calamagrostis chrysantha showed elevated Ca and P concentrations. Correlation analysis revealed strong positive associations among Ca, Mg, Cu, Fe, Mn, and Zn (r = 0.7-1.0), indicating coordinated uptake mechanisms. Nickel exhibited negative correlations with P (r = -0.6) and K (r = -0.5). Improved species require intensive fertilization, while native species offer sustainable alternatives for low-input high-altitude systems.
Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery
(MDPI, 2023-06-20) Pizarro Carcausto, Samuel Edwin; Pricope, Narcisa G.; Figueroa Venegas, Deyanira Antonella; Carbajal Llosa, Carlos Miguel; Quispe Huincho, Miriam Rocío; Vera Vilchez, Jesús Emilio; Alejandro Méndez, Lidiana Rene; Achallma Mendoza, Lino; González Tovar, Izamar Estrella; Salazar Coronel, Wilian; Loayza, Hildo; Cruz Luis, Juancarlos Alejandro; Arbizu Berrocal, Carlos Irvin
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.
Integrated assessment of water quality and trophic status in High Altitude Andean lagoons: a multi-index and multivariate approach for sustainable fish farming management
(Elsevier Inc., 2026-05-27) Custodio, María; Huarcaya, Javier; Ccopi Trucios, Dennis; Alvarez, Daniel; Pizarro Carcausto, Samuel Edwin; Ortega Quispe, Kevin Abner
High-altitude Andean lagoons are ecologically sensitive systems whose water quality is under increasing pressure from the expansion of aquaculture and other human activities. An integrated assessment of water quality and trophic status was carried out in three lagoons located in the highlands of central Peru, using a multi-index and multivariate approach that combined physicochemical parameters, trophic indices (TRIX, TSI, molar N:P ratio), and heavy-metal indices, evaluated against Peruvian EQS, USEPA, and CCME water-quality standards. Principal component analysis explained 60.1% of the total variability, with the first axis (40.4%) structured by an organic loading and nutrient gradient that consistently separated Tipicocha from the other lagoons. Total phosphorus in Tipicocha reached concentrations well above the Peruvian EQS threshold, and TRIX values classified all lagoons as eutrophic to hypertrophic (5.46–6.21). TSI (TP) classified Tipicocha and Tranca Grande as hypereutrophic, whereas TSI (Chl-a) remained within the eutrophic range (53–60), a pattern consistent with additional environmental constraints on phytoplankton biomass. Molar N:P ratios indicated strong nitrogen limitation in Tipicocha and Tranca Grande, whereas Pomacocha showed a seasonal shift from co-limitation during the dry season to potential nitrogen limitation during the rainy season. Metal contamination was low according to EQS and USEPA criteria, whereas CCME thresholds suggested moderate to high contamination (HPI up to 105.1), with systematic exceedances of Cd, Cu, and Zn at all sites. Taken together, the results point to strong trophic enrichment associated with fish-farming activity as the main pressure on water quality in these ecosystems and show that the choice of regulatory framework has a decisive influence on metal-risk classification.
Moderate deficit irrigation improves agronomic performance of quinoa (Chenopodium quinoa Willd.) compared to full irrigation in the central highlands of Peru
(Learning Gate, 2026-03-19) Gavino Lulo, Esthefany Irene; Ccopi Trucios, Dennis; Garcia Seguil, Erika Janina; Requena Rojas, Edilson Jimmy; Contreras, Jose; Solórzano Acosta, Richard Andi; Betega, S.D.; Yaranga, Raúl M.; Pizarro Carcausto, Samuel Edwin
This study evaluated the agronomic performance and water productivity of quinoa (Chenopodium quinoa Willd. cv. INIA 433) under three irrigation regimes in the central highlands of Peru: optimal irrigation (Ks = 1.00), moderate deficit (Ks = 0.66), and severe deficit (Ks = 0.49). The experiment combined constant water table lysimeters and field plots, integrating crop coefficient estimation, water balance analysis, and multispectral monitoring (NDVI, NDRE, SRWI) using UAV imagery and ground spectroradiometry. Moderate water stress (Ks = 0.66) significantly improved reproductive performance, producing approximately 8,000 grains per plant compared with ~3,900 grains per plant under optimal irrigation. Grain protein content increased from 4.8% to 6.0%, while evapotranspiration decreased by 37% (from 374.5 to 234.4 mm), markedly improving water use efficiency. In contrast, optimal irrigation promoted maximum vegetative growth (plant height ~110 cm; NDVI 0.7–0.8) but lower reproductive output, whereas severe stress (Ks = 0.49) reduced yield to 4,400 grains per plant and accelerated senescence. Multispectral indices effectively distinguished water stress levels: NDVI reflected canopy vigor, NDRE detected chlorophyll variation, and SRWI captured plant water status. The results demonstrate that regulated deficit irrigation enhances water productivity and grain quality in quinoa. Maintaining Ks values around 0.65–0.70 appears to optimize yield and resource use efficiency in water-limited Andean agroecosystems.
Simulation of soil organic carbon potential sequestration for high Andes Peruvian croplands
(Sociedade Brasileira de Ciência do Solo, 2025-10-06) Carbajal Llosa, Carlos Miguel; Vera Vílchez, Jesús Emilio; Pizarro Carcausto, Samuel Edwin; Mestanza, Carlos
Soil organic carbon (SOC) sequestration in croplands represents a significant opportunity to mitigate climate change by removing carbon dioxide from the atmosphere. Simulation tools are increasingly used to assess the impact of climate change and soil management on soil organic carbon stock dynamics. Although Andean soils typically store large amounts of organic carbon, agricultural practices, especially plowing, may deplete these stocks, creating a need to understand these dynamics better. Here, we show the soil organic carbon sequestration potential in croplands in the Peruvian Andean region over 50 years. Soil organic carbon content and bulk density were spatially predicted across the study area using 100 georeferenced soil samples to quantify organic carbon stocks. Spatial interpolation was performed using Ordinary Kriging with exponential and spherical variogram models, which provided the best fit to the data. The RothC model was used to simulate changes in soil organic carbon stocks under two contrasting agricultural management scenarios: one without manure application and another with annual application of one ton of manure per hectare. We found that manure application can substantially increase soil organic carbon sequestration in croplands with increases ranging from 105.22 to 214.94 Mg ha-¹ over 50 years. The potential for increased carbon sequestration through manure application could help compensate for losses in other areas of the watershed, particularly grasslands (74.4 % of the area). This study contributes valuable information for developing sustainable land management strategies in Andean agroecosystems.
Study of ecosystem degradation dynamics in the Peruvian Highlands: Landsat time-series trend analysis (1985–2022) with ARVI for different vegetation cover types
(MDPI, 2023-10-31) Cano, Deyvis; Pizarro Carcausto, Samuel Edwin; Cacciuttolo, Carlos; Peñaloza, Richard; Yaranga Cano, Raul Marino; Gandini, Marcelo Luciano
The high-Andean vegetation ecosystems of the Bombón Plateau in Peru face increasing degradation due to aggressive anthropogenic land use and the climate change scenario. The lack of historical degradation evolution information makes implementing adaptive monitoring plans in these vulnerable ecosystems difficult. Remote sensor technology emerges as a fundamental resource to fill this gap. The objective of this article was to analyze the degradation of vegetation in the Bombón Plateau over almost four decades (1985–2022), using high spatiotemporal resolution data from the Landsat 5, 7, and 8 sensors. The methodology considers: (i) the use of the atmosphere resistant vegetation index (ARVI), (ii) the implementation of non-parametric Mann–Kendall trend analysis per pixel, and (iii) the affected vegetation covers were determined by supervised classification. This article’s results show that approximately 13.4% of the total vegetation cover was degraded. According to vegetation cover types, bulrush was degraded by 21%, tall grass by 18%, cattails by 16%, wetlands by 14%, and puna grass by 13%. The Spearman correlation (p < 0.01) determined that degraded covers are replaced by puna grass and change factors linked with human activities. Finally, this article concludes that part of the vegetation degradation is related to anthropogenic activities such as agriculture, overgrazing, urbanization, and mining. However, the possibility that environmental factors have influenced these events is recognized.
The effects of the inoculation of bacterial microorganisms (Pseudomonas sp. and Bacillus sp.) on soil quality, aerial biomass and nutritional quality of native grasses under field conditions in the Peruvian highlands
(Soil Science Society of Poland, 2026-04-15) Arias Arredondo, Alberto Gilmer; Pizarro Carcausto, Samuel Edwin; Requena Rojas, Edilson Jimmy; Verástegui Martínez, Patricia; Cruz Luis, Juancarlos Alejandro; Solórzano Acosta, Richard Andi
Peruvian highland ecosystems cover approximately 22 million hectares and provide key ecosystem services that support human well-being and food security. Soil functioning in these ecosystems largely depends on the activity of microbial communities. This study evaluated the effects of Pseudomonas sp. and Bacillus sp. inoculation on soil chemical properties, aerial biomass production, and nutritional quality of Festuca dolichophylla, Jarava ichu and Cinnagrostis vicunarum. A field experiment was conducted at 4379 m a.s.l. in the central Peruvian highlands. Bacterial inoculation increased soil organic matter and nitrogen availability in plots dominated by J. ichu and F. dolichophylla inoculated with Bacillus sp., compared to non-inoculated controls. Higher soil phosphorus content was observed in C. vicunarum pastures inoculated with Pseudomonas sp. In terms of biomass production, significant increases were recorded in C. vicunarum under both bacterial inoculations and in F. dolichophylla associated with Bacillus sp., while J. ichu showed higher yields with Pseudomonas sp. In addition, bacterial inoculation improved forage nutritional quality, particularly total protein, calcium, and phosphorus contents in J. ichu, highlighting species-specific plant–microorganism interactions. Overall, the inoculation of beneficial bacteria represents a promising and environmentally sustainable strategy to improve soil quality, forage productivity, and nutritional value in native highland grasslands, contributing to more resilient rangeland systems and the conservation of ecosystem services.
Variación espacial de la fertilidad del suelo en la EEA Santa Ana
(Instituto Nacional de Innovación Agraria (INIA), 2026-02-10) Quispe Matos, Kenyi Rolando; Carbajal Llosa, Carlos Miguel; Mejia Maita, Sharon Yahaira; Fernandez Puquio, Albert Einstein; Mercado Chinchay, Ruth Lizbeth; Ore Valeriano, Ruddy Adely; Pizarro Carcausto, Samuel Edwin; Alejandro Mendez, Lidiana Rene; Solórzano Acosta, Richard Andi; Cruz Luis, Juancarlos Alejandro
La degradación de los suelos en las regiones altoandinas del Perú constituye un problema relevante debido a su impacto directo en la productividad agrícola y la sostenibilidad de los sistemas productivos. La pérdida progresiva de cobertura vegetal contribuye significativamente a este proceso, dado que incrementa la erosión, disminuye la capacidad de retención de agua y compromete la estructura física del suelo (Vanacker et al., 2022). Esta situación se ve intensificada por la alta variabilidad de la fertilidad del suelo y por las prácticas de manejo inadecuadas que reducen la eficiencia de los fertilizantes (Quispe et al., 2024). Asimismo, las bajas temperaturas, propias de estos ecosistemas, reducen la velocidad de descomposición de la materia orgánica, lo que favorece la acumulación de carbono orgánico en el suelo y retrasa la mineralización de nutrientes, limitando su disponibilidad inmediata para los cultivos (Liu et al., 2025). Esta dinámica es propia de los ecosistemas fríos y debe considerarse en el manejo de la fertilidad, dado que influye directamente en la respuesta de los sistemas agrícolas y en la eficiencia de las prácticas de fertilización. En la provincia de Huancayo, departamento de Junín, la degradación de suelos se ha intensificado como consecuencia de un acelerado proceso de urbanización registrado en las últimas décadas, impulsado por factores económicos, demográficos y sociales. A ello, se suma la deposición atmosférica de elementos tóxicos provenientes de zonas mineras, la cual ha promovido la conversión de tierras agrícolas en áreas urbanas. Como consecuencia, la cobertura vegetal y la capacidad natural de almacenamiento de agua se ha reducido, deteriorando la calidad ambiental y acelerando la degradación del suelo (Haller, 2017). Frente a este escenario, el monitoreo continuo de la fertilidad del suelo se vuelve esencial para planificar la producción agrícola de manera sostenible y corregir oportunamente deficiencias o excesos en los parámetros edáficos. Sin embargo, la carencia de herramientas técnicas que permitan una interpretación espacial precisa de las propiedades fisicoquímicas del suelo, limita la toma de decisiones en la fertilización de los cultivos. La integración de enfoques de agricultura de precisión, geoestadística, análisis de suelos y sistemas de información geográfica (SIG) permiten abordar esta limitación. El uso de métodos como la interpolación kriging, índice de Moran y análisis de variogramas facilitan la identificación de patrones espaciales, mejoran la interpretación de la variabilidad edáfica y aportan información clave para la gestión diferenciada de los suelos. Esta información constituye una base sólida para diseñar estrategias de fertilización por zonas, optimizando el uso de insumos y contribuyendo a la conservación de la salud del suelo a largo plazo (Culman et al., 2021; Chinea-Horta y Rodríguez-Izquierdo, 2021). En este contexto, el presente manual tiene como objetivo evaluar la fertilidad del suelo y su variación espacial en la Estación Experimental Agraria Santa Ana, con la finalidad de generar información técnica que contribuya a la planificación agrícola y al fortalecimiento de los sistemas productivos de la región. Para ello, se propone diagnosticar el estado actual de la fertilidad del suelo e identificar sus principales limitantes; elaborar mapas de variabilidad espacial de las propiedades edáficas que permitan reconocer diferencias en la calidad del suelo; así como formular estrategias de manejo de la fertilidad orientadas a optimizar el uso de fertilizantes y enmiendas, incrementar la productividad, y promover la sostenibilidad de los sistemas agrícolas y pecuarios de la estación.
Vis-NIR spectroscopy and machine learning for prediction of soil fertility indicators and fertilizer recommendation in Andean highland and rainforest agroecosystems
(MDPI, 2026-04-26) Pizarro Carcausto, Samuel Edwin; Ccopi Trucios, Dennis; Ortega Quispe, Kevin Abner; Contreras Pino, Duglas Lenin; Ñaupari, Javier; Cano, Deyvis; Patricio Rosales , Solanch Rosy; Loayza, Hildo; Apolo Apolo, Orly Enrique
This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R2 > 0.70), while pH and texture fractions showed moderate performance (R2 = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K2O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients.