Jose Otoya-Barrenechea

Rural areas in the Peruvian Amazon exhibit profound structural gaps, yet traditional development measurements often rely on linear, compensatory approaches that mask simultaneous deprivations. Achieving the Sustainable Development Goals (SDGs) in these territories requires comprehensive diagnostic tools adapted to complex local realities. This study proposes the Multidimensional Local Development Index (MLDI) for 63 rural communities in the Amazonas region in Peru. Utilizing data from the 2024 National Agricultural Survey and fieldwork, an initial set of 29 candidate indicators across six dimensions (Population, Social Welfare, Economy, Environment, Technology, and Production) was identified and validated via the Delphi method. Following a variance-based proportional weighting, the 8 most statistically discriminant indicators were retained and aggregated using a geometric mean to explicitly prevent compensatory biases. The MLDI revealed a generalized scenario of “Low” to “Very Low” development (scores ranging from 0.11 to 0.37), with no communities achieving medium or high levels. The non-compensatory aggregation exposed a critical territorial dynamic: the “illusion of productivity”. While some settlements showed advances in agricultural output or internet access, severe structural bottlenecks prevented these from translating into economic well-being. The study identified significant transmission friction between productive volume and household income. The results suggest that sectoral interventions focused solely on increasing agricultural yield or digital connectivity are insufficient to reduce rural poverty. To achieve sustainable development, public policies must shift toward integrated territorial management, ensuring that productivity gains are strictly paired with infrastructure, market access, and institutional capacity.

National dairy sustainability monitoring requires indicators that are scientifically defensible, policy-relevant, and feasible to construct from recurrent agricultural surveys. The aim of this study was to construct, interpret, and evaluate a national, survey-weighted, frame-aware, MESMIS-informed monitoring profile for Peruvian dairy cattle producers using ENA 2021–2024 microdata. The Peruvian National Agricultural Survey (Encuesta Nacional Agropecuaria, ENA) was explicitly treated as a source of proxy-based sustainability monitoring rather than as a complete farm sustainability assessment. Using survey design variables and harmonized items on livestock, markets, producer capacity, and resource management, the analysis constructed survey-weighted formative profiles for nationally monitored dairy cattle producer units. The analytical sample comprised 42,331 agricultural producer units selected based on complete interview status, reported livestock activity, and harmonized evidence of dairy cattle activity. Because ENA documentation indicates a sampling-frame transition in 2023, the 2021–2022 and 2023–2024 rounds were treated as separate sampling-frame regimes. Indicators were scaled to 0–1 formative profiles, with explicit checks for missingness, indicator availability, exclusion of derivative outputs, exclusion of producer age, minimum-availability thresholds, and sampling-regime-specific scaling. The median indicator availability was 12 of 14 economic and productive indicators, five of five social access and capacity indicators, and eight of eight environmental and resource-management indicators. Survey-weighted descriptive estimates and adjusted within-regime contrasts showed that the strongest signal was a lower social access and capacity profile in 2022 than in 2021, mainly associated with lower agricultural information use, livestock training, and technical assistance. Within the 2023–2024 sampling-frame regime, the main adjusted profiles were statistically stable. The results show that ENA can support a reproducible national monitoring baseline, provided that the resulting profiles are interpreted as proxy-based formative summaries constrained by survey coverage rather than as direct environmental performance scores, causal trends, or complete sustainability measures.

Subsistence agriculture persists across Peru with markedly heterogeneous patterns among ecological regions, yet multi-regional analyses using predictive methods remain scarce. This study identified determinants of household subsistence using random forest and gradient boosting classifiers. Microdata from the 2024 Peruvian National Agricultural Survey yielded 34,827 agricultural units after quality control. Subsistence was operationalised as the share of self-consumption in total production value and categorised as low (0% - 20%), medium (20% - 60%) and high (> 60%). On a held-out test set of 10,448 units both algorithms converged (random forest 82.9%, gradient boosting 82.6% accuracy), yet a nested sensitivity analysis showed that this performance depends substantially on predictors that enter the definition of the dependent variable. Once income-derived and market-participation variables were removed, accuracy settled at 65.7% against a 57.9% majority-class baseline, and harvested area and input expenditure emerged as the leading structural determinants. Three findings run against prevailing narratives. First, productive richness does not accompany commercialisation: Andean units combined the highest richness (7.9 versus 5.8 species on the Coast) with the highest subsistence (39.1% versus 15.0%). Second, direct-to-consumer selling was associated with higher subsistence (34.2%) than collector intermediation (14.3%), consistent with intermediaries operating as a pragmatic commercialisation route rather than a barrier. Third, accuracy was lowest precisely in the Andes (77.0%), where subsistence concentrates, and weakest for the transitional medium class (60.7%). Reporting predictive performance without separating definitional from structural predictors overstates what such models explain, a distinction rarely made in this literature.

This paper examines the role of agricultural credit in shaping farm performance and technology-related outcomes in Peru, using nationally representative microdata from the Encuesta Nacional Agropecuaria (ENA). In a context characterized by credit rationing and institutional constraints, access to finance may influence agricultural income, productivity, and the adoption of improved practices through multiple direct and indirect channels. To address the non-random allocation of credit, the analysis employs a quasi-experimental framework that combines propensity score trimming, block-based common support restrictions, entropy balancing, and doubly robust treatment-effect estimators (IPWRA and AIPW). Descriptive evidence documents substantial heterogeneity in credit sources, loan uses, and rejection reasons, highlighting structural barriers related to collateral, land tenure, and risk. Regression results on the balanced sample indicate positive and statistically significant associations between credit access and both real agricultural income and land productivity. However, estimated treatment effects are sensitive to the estimation strategy: while IPWRA estimates suggest economically meaningful gains among credit recipients, AIPW estimates are smaller and not always statistically distinguishable from zero. Exploratory results further suggest that credit access is positively associated with technology adoption and managerial capacity, consistent with, but not identifying, a potential association between credit approval and technological practices. Overall, the findings are consistent with a growing body of evidence showing that the impacts of agricultural credit are modest, heterogeneous, and context dependent. From a sustainability perspective, the results underscore the importance of complementary interventions—such as land tenure security, risk management instruments, and tailored financial services—in enhancing the effectiveness of rural credit programs in agricultural systems characterized by imperfect markets and high production risk.

Subsistence family farming in Peru is increasingly constrained by ecosystem degradation, climate variability, and limited access to productive services, particularly where environmental exposure is high. This study develops an Agro-productive and Territorial Vulnerability Index (IVAPT) to evaluate environmental, ecosystem, and socioeconomic vulnerability of subsistence agriculture at the district level nationwide. The index integrates district-level agricultural survey data (ENA-2024) with multi-temporal MODIS NDVI series (2000–2024) and comprehensive climatic, topographic, land-cover, and accessibility indicators, processed through multivariate statistics. Three objective weighting schemes (ENTROPY, CRITIC, PCA) construct thematic sub-indices of Environmental Exposure (EnvExp), Ecosystem Condition (EcoCond), and Socioeconomic Capacity (SocioCap). Results show more than half of Peru’s 1552 districts fall within moderate to very high vulnerability, with highest concentration in the Amazon region (Loreto, Ucayali, Madre de Dios), Andean-Amazonian transitions, and highland districts (Huancavelica, Apurímac, Ayacucho, Puno) where biophysical constraints, ecosystem pressure, and socioeconomic isolation converge. Dimensional spatial complementarity EnvExp peaking on coast, EcoCond in Amazon, SocioCap in Andes demonstrates effective vulnerability reduction requires dimension-specific interventions. Despite divergent weighting schemes, spatial patterns remained consistent, validating identified hotspots. IVAPT provides a reproducible framework supporting evidence-based territorial planning and targeted investments in water infrastructure, ecosystem restoration, and climate adaptation.

El presente estudio analiza la relación entre la percepción de la gestión del talento humano y el nivel de satisfacción laboral en el personal no docente de la Universidad Nacional Agraria La Molina (UNALM), para lo cual emplea un diseño no experimental de tipo básico y correlacional. Mediante la aplicación de un cuestionario estructurado basado en la escala Likert a una muestra de 120 trabajadores, se obtuvieron resultados que, a través de la prueba de correlación de Spearman, evidencian una relación positiva y significativa entre ambas variables (Rho = 0.347; p = 0.000). Entre las dimensiones evaluadas, la de compensación y beneficios mostró la correlación más alta con la satisfacción laboral (Rho = 0.365, p = 0.000), seguida del trabajo en equipo (Rho = 0.202; p = 0.027); mientras que la calidad de vida laboral no presentó una relación significativa (Rho = 0.167; p = 0.069), lo que sugiere que otros factores podrían estar incidiendo en el nivel del bienestar laboral. Estos hallazgos refuerzan la idea de que una gestión efectiva del talento humano es clave para mejorar la satisfacción del personal administrativo, y que la compensación económica y los beneficios son los elementos más influyentes. En consecuencia, se recomienda optimizar los sistemas de remuneración, fomentar una cultura organizacional basada en el trabajo en equipo y explorar estrategias adicionales que contribuyan a mejorar la calidad de la vida laboral dentro de la institución, con el objetivo de fortalecer el compromiso y la motivación del personal.

Esta investigación analiza el impacto del modelo de negocios de AGROBANCO en la calidad de vida de los pequeños productores agropecuarios en la Región de San Martín. Con un enfoque cuantitativo y diseño no experimental de corte transversal, se encuestó a una muestra de 96 productores de la cartera de AGROBANCO para evaluar tres aspectos del servicio: la especialización del personal, la función del gestor de negocios y la herramienta “hoja de producto” para la evaluación crediticia. Los resultados indican una percepción positiva del personal de atención, aunque con áreas de mejora en visitas técnicas y asesoramiento en innovaciones. La función del gestor de negocios fue bien valorada, pero se identificaron oportunidades de mejora en el servicio postventa. La herramienta “hoja de producto” recibió percepciones mixtas en su comprensión, pero se valoraron positivamente el cronograma de pago y la adaptabilidad de los términos. Se concluye que el modelo de negocio de AGROBANCO influye positivamente en la percepción de calidad de vida de los pequeños productores, con una correlación positiva moderada a fuerte (Rho: 0.435) y altamente significativa (p-value: 0.000), indicando que la planificación estratégica y la gestión empresarial de AGROBANCO tienen un impacto positivo en la vida de estos productores, mejorando sus posibilidades empresariales y contribuyendo al desarrollo comunitario.