Search Results (308)

Dasiops saltans is a small insect pest associated with pitahaya cultivation, whose occurrence is strongly influenced by specific environmental conditions. This study examined where this species could live in the Amazonas region by using models that identify areas with favourable conditions. With this approach, the current and future distribution of the insect was estimated, considering possible changes in climate. The results show that the places with the best conditions for the species may decrease slightly in the coming decades, while most of the region will continue to be unfavorable for its presence. The study also identified which environmental factors most influence where the insect can survive, highlighting the role of the terrain, soil characteristics and climate conditions related to temperature and moisture. These findings help us better understand the environmental limits of Dasiops saltans and provide useful information for decision-makers, farmers and local authorities, who can use this knowledge to improve management, monitoring and prevention strategies in agricultural areas. Full article

The environmental and social crises affecting global agri-food systems highlight the need for a profound transformation of production models and their territorial relations. In this context, agroecology, understood as science, practice, and movement, has emerged as a paradigm capable of integrating ecological sustainability, social equity, and community participation. Within this framework, the work carried out by CREA in the AgrEcoMed project (new agroecological approach for soil fertility and biodiversity restoration to improve economic and social resilience of Mediterranean farming systems), funded by the PRIMA programme, investigates agroecology as a social and political process of territorial regeneration. This process is grounded in co-design with local stakeholders, collective learning, and the construction of multi-actor networks for agroecology in the Mediterranean. The Manifesto functions as a tool for participatory governance and value convergence, aiming to consolidate a shared vision for the Mediterranean agroecological transition. The article examines, through an analysis of the existing literature, the role of agroecological networks and empirically examines the function of the collective co-creation of the Manifesto as a tool for social innovation. The methodology is based on a participatory action-research approach that used local focus groups, World Café, and thematic analysis to identify the needs of the companies involved. The results highlight the formation of a multi-actor network currently comprising around 90 members and confirm the effectiveness of the Manifesto as a boundary object for horizontal governance. This demonstrates how sustainability can emerge from dialogue, cooperation, and the co-production of knowledge among local actors. Full article

Efficient irrigation planning in data-scarce regions remains challenging due to limited access to localized meteorological data, reliance on complex computer-based models, and the technical knowledge required to deploy them at the field scale. Hence, the need for accessible, smartphone-based tools that simplify soil water balance calculations using public data to support practical decision-making in resource-limited contexts. This smartphone-based application estimates Net and Gross Irrigation Requirements using a Soil Water Balance (SWB) framework. The app combines region-specific empirical formulations for Effective Rainfall (Pe) calculation. The application utilizes user-supplied crop and irrigation parameters and meteorological data available in the public domain and operates at multiple temporal scales (daily, 10-day, weekly, and monthly), thereby supporting flexible irrigation schedules. The performance of app was evaluated through simulation-based benchmarking against FAO-CROPWAT 8.0 using harmonized inputs across five representatives agro-climatic region: Central India, Southern Vietnam, Northern Thailand, Western Bangladesh, and Central Sri Lanka. Quantitative comparison showed deviations within ±5% for Effective Rainfall, crop evapotranspiration, Net Irrigation, and Gross Irrigation, and low mean bias values (−2.8% to +3.3%) show the absence of systematic over- or under-estimation compared to CROPWAT model. The application also demonstrated responsiveness to climatic variability. Although the validation is limited to few representative locations and assumed minimal runoff conditions, the results suggest that the proposed method is technically consistent and feasible in practice. This study demonstrates smartphone-based application as a decision support for field-level irrigation planning and water resource management, particularly in data-limited agricultural contexts. Full article

Microplastics (MPs)—synthetic polymer particles less than 5 mm in size—have emerged as ubiquitous contaminants in terrestrial and aquatic environments worldwide, raising concerns about their ecological and human health impacts. While research has predominantly focused on urban and marine settings, evidence shows that rural ecosystems are also affected, challenging assumptions of pristine conditions outside cities and coasts. This review synthesizes current knowledge on the presence, pathways, and impacts of MPs in rural environments, highlighting complex contamination dynamics driven by both local sources (agricultural plastics, domestic waste, rural wastewater, and road runoff) and regional processes (atmospheric deposition, hydrological transport, and sediment transfer). Key findings highlight that rural lakes, streams, soils, and groundwater systems are active sinks and secondary sources of diverse MPs, predominantly polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET) in fibrous and fragmented forms. These particles vary in size, density, and color, influencing their transport, persistence, and bioavailability. Ecological effects include bioaccumulation in freshwater species, soil degradation, and potential food chain transfer, while human exposure risks stem from contaminated groundwater, air, and locally produced food. Despite these growing threats, rural systems remain underrepresented in monitoring and policy frameworks. The article calls for context-specific mitigation strategies, enhanced wastewater treatment, rural waste management reforms, and integrated microplastics surveillance across environmental compartments. Full article

Agroecology is increasingly shaped by the convergence of traditional knowledge, farmers’ lived experiences, and scientific research, fostering a plural dialog that embraces the ecological and socio-political complexity of agricultural systems. Within this framework, soil biodiversity is essential for maintaining ecosystem functions, with soil microbiology, and particularly arbuscular mycorrhizal fungi (AMF), playing a pivotal role in enhancing soil fertility, plant health, and agroecosystem resilience. This review explores the synergy between agroecological practices and AMF by examining their ecological, economic, epistemic, and territorial contributions to sustainable agriculture. Drawing on recent scientific findings and Latin American case studies, it highlights how practices such as reduced tillage, crop diversification, and organic matter inputs foster diverse and functional AMF communities and differentially affect their composition and ecological roles. Beyond their biological efficacy, AMF are framed as relational and socio-ecological agents—integral to networks that connect soil regeneration, food quality, local autonomy, and multi-species care. By bridging ecological science with political ecology and justice in science-based knowledge, this review offers a transdisciplinary lens on AMF and proposes pathways for agroecological transitions rooted in biodiversity, cognitive justice, and territorial sustainability. Full article

Evapotranspiration (ET) is challenging to measure directly, motivating the use of remote sensing products as alternatives. We evaluated five high-resolution (≤1 km) global ET products (SSEBop, MOD16, ETMonitor, PMLv2, and FAO’s WaPOR) against five eddy covariance (EC) measurements in Morocco’s semi-arid Tensift Al-Haouz region, with observations spanning from 2006 to 2019. These five products were selected because they offer the finest spatial resolution (around 1 km or less) among freely downloadable global ET datasets, making them well-suited for comparison with local EC flux tower data. The study area was chosen for its reliable ground-truth EC stations, extensive knowledge of local irrigation practices, and a semi-arid climate that provides a rigorous testbed for ET model evaluation in water-limited conditions. Precipitation observations were included to assess each product’s sensitivity to soil moisture and precipitation-driven ET variations, particularly to identify which models respond to rainfall and irrigation inputs (i.e., differences between rainfed and irrigated fields). Results indicate that PMLv2 achieved the best agreement with EC (R² up to 0.65, RMSE as low as 0.4 mm/day, and PBIAS under 10% at most sites), followed by WaPOR and SSEBop which captured seasonal ET patterns (R² ~0.3–0.5) with moderate bias (~20–30%). In contrast, ETMonitor and MOD16 underperformed, showing larger errors (RMSE ~1–2.5 mm/day) and substantial underestimation biases (e.g., MOD16 PBIAS ~50–80% in irrigated sites). These findings underscore the impact of algorithmic differences and highlight PMLv2, SSEBop, and WaPOR as more reliable options for estimating ET in semi-arid agricultural regions lacking in situ measurements. Full article

Building upon a theoretical framework, this study utilized 126 field survey questionnaires from farmers in Shouyang County, Shanxi Province, China, coupled with corresponding farmland soil heavy metal monitoring data, to investigate the extent of heavy metal pollution and its mechanistic relationship with farmers’ behavior. The single-factor pollution index (P_i), Nemerow composite pollution index (P_N), and geographical detector were employed to assess pollution levels and elucidate the underlying mechanisms linking farmer practices to soil heavy metal accumulation. Analysis revealed that the mean concentrations of Cu, Ni, Cr, Pb, Cd, and Zn (25.54, 31.47, 98.50, 16.63, 0.16 and 76.92 mg/kg, respectively) in the farmland soil exceeded the background values for soil elements in Shanxi Province, whereas As (1.92 mg/kg) levels were lower. Assessment using P_i indicated that Cr, Pb, Cd, Ni, Cu, and Zn (1.78, 1.13, 1.55, 1.05, 1.07 and 1.21, respectively) were predominantly in a state of mild pollution. Similarly, the P_N (1.50) suggested an overall mild level of composite heavy metal pollution in the soil. Geographical detector(Geo-Detector) analysis demonstrated that the explanatory power (q-value) of interactions among factors-including agricultural film and fertilizer application intensity, farmland fragmentation degree, per capita annual household income, farmland area, and years engaged in farming-on soil heavy metal accumulation was significantly enhanced compared to that of individual behavioral factors. While individual farmers’ behaviors are associated with heavy metal accumulation, the interaction effects among multiple behaviors constitute the dominant factor influencing localized accumulation in farmland soil. Consequently, local authorities should enhance farmers’ requisite knowledge, skills, and practices for mitigating soil heavy metal accumulation through strategies such as promoting large-scale farming, implementing agricultural input reduction initiatives, and intensifying technical and environmental protection training. The Geo-Detector exhibits significant advantages in identifying nonlinear influencing factors and analyzing factor interactions, yielding more comprehensive insights compared to conventional linear models. Full article

Precision technologies are increasingly relevant in contemporary agriculture, offering tools to enhance efficiency, sustainability, and decision-making. Their adoption is becoming particularly critical among vine-growers in the wine industry, a sector facing market pressures, climate change, and generational shifts. This study combines a systematic literature review with an empirical analysis of the PDO (Protected Designation of Origin) Wines of Granada (Southern Spain) to examine perceptions of precision agriculture technologies at both global and regional scales. The review included 607 articles published between 2015 and 2025 in English (indexed in ISI Web of Knowledge), identifying key factors influencing technology adoption. Using “perception” and “precision agriculture” as search terms, only 97 articles simultaneously addressed both concepts. At the regional level, a case study involving 22 wineries (with 37 stakeholders) in Granada province was conducted, focusing on socioeconomic barriers and environmental conditions such as altitude, climate, and soil type. Results revealed cross-scale consistencies regarding the importance of costs and perceived usefulness of new technologies (e.g., proximal sensors, satellite imagery), but divergences concerning the difficulties in accessing them and their cost. The findings highlight the need for supportive policies, targeted training, and practical demonstrations to facilitate adoption, thereby fostering innovation and sustainability, especially in the wine sector of the province of Granada. Integrating international and local evidence provides a framework for designing regional strategies tailored to promote precision technologies that improve efficiency, quality, and sustainability in wine production. Full article

In Saudi Arabia, coffee (Coffea arabica L.) has been grown for centuries on the mountain terraces of the southwestern regions. Jazan region accounts for about 80% of the total production. The acreage allocated to coffee is comparatively small but it is expanding rapidly thanks to a strong government-supported drive to increase local coffee production. Despite the initial success, the effort is hampered by the limited water supply available for irrigating the new plantings and the increased incidence of pests and diseases. The magnitude of these natural handicaps appears to have increased as of late, apparently due to climate change (CC). This review examines strategies to mitigate the consequences of CC on the coffee sector through the implementation of precision agriculture (PA) techniques, with the focus on addressing the challenges posed by biotic and abiotic stresses. The impact of CC is both direct by rendering present growing regions unsuitable and indirect by amplifying the severity of biotic and abiotic tree stressors. Precision agriculture (PA) techniques can play a key role in tackling these challenges through data-driven tools like sensors, GIS, remote sensing, machine learning and smart equipment. By monitoring soil, climate, and crop conditions, PA enables targeted irrigation, fertilization, and pest control thus improving efficiency and sustainability. This approach reduces costs, conserves resources, and minimizes environmental impact, making PA essential for building climate-resilient and sustainable coffee production systems. The review synthesizes insights from case studies, research papers, and other scientific literature concerned with precision farming practices and their effectiveness in alleviating biotic and abiotic pressures on coffee trees. Additionally, it evaluates technological advances, identifies existing knowledge gaps, and suggests areas for future research. Ultimately, this study seeks to contribute to enhancing the resilience of coffee farming in Saudi Arabia amidst ongoing CC challenges by educating farmers about the potential of PA technologies. Full article

This study investigated the application of Species Distribution Models (SDMs), based on Boosted Regression Tree (BRT) and Random Forest (RF), to predict the distribution of citrus crops in a Mediterranean climate by comparing climate data from WorldClim with those from the Regional Territorial Information System of Sicily (S.I.T.R.). To this aim, 19 bioclimatic variables were calculated from monthly temperature and precipitation data in the period 2003–2021 by using the biovars package in R software version 2023.12.0+369. Soil properties, terrain elevation, slope, and soil water retention capacity were considered to adequately simulate pedoclimatic conditions in the Syracuse area in Sicily (Italy). The SDM algorithms performed well (AUC: 0.84–0.93; TSS: 0.51–0.69), and Random Forest was selected to compare global and local outcomes. Using data from local meteorological stations increased the model’s reliability, resulting in a difference of approximately ~800 ha in the predicted citrus distribution compared to WorldClim data. This approach also provided a more accurate representation of precipitation patterns, for instance, in the municipality of Augusta, where WorldClim underestimated the average annual rainfall by 284 mm. These findings emphasise the importance of incorporating local environmental data into SDMs to improve prediction accuracy and inform future hybrid approaches to enhance model robustness in the context of climate change. Finally, the results contribute to expanding knowledge of citrus soil and climate conditions, with potential implications for land-use planning. Full article

This study presents a novel federated learning (FL) methodology implemented directly on STM32-based microcontrollers (MCUs) for energy-efficient smart irrigation. To the best of our knowledge, this is the first work to demonstrate end-to-end FL training and aggregation on real STM32 MCU clients (STM32F722ZE), under realistic energy and memory constraints. Unlike most prior studies that rely on simulated clients or high-power edge devices, our framework deploys lightweight neural networks trained locally on MCUs and synchronized via message queuing telemetry transport (MQTT) communication. Using a smart agriculture (SA) dataset partitioned by soil type, 7 clients collaboratively trained a model over 3 federated rounds. Experimental results show that MCU clients achieved competitive accuracy (70–82%) compared to PC clients (80–85%) while consuming orders of magnitude less energy. Specifically, MCU inference required only 0.95 mJ per sample versus 60–70 mJ on PCs, and training consumed ∼70 mJ per epoch versus nearly 20 J. Latency remained modest, with MCU inference averaging 3.2 ms per sample compared to sub-millisecond execution on PCs, a negligible overhead in irrigation scenarios. The evaluation also considers the payoff between accuracy, energy consumption, and latency through the Energy Latency Accuracy Index (ELAI). This integrated perspective highlights the trade-offs inherent in deploying FL on heterogeneous devices and demonstrates the efficiency advantages of MCU-based training in energy-constrained smart irrigation settings. Full article

Smallholder farming plays a crucial role in global food security, contributing more than half of the world’s food supply. However, it is increasingly threatened by climate variability, declining soil fertility, and financial constraints, all of which suppress plant growth, reduce yields, and endanger livelihood stability. Addressing these challenges requires sustainable, eco-friendly alternatives to costly and environmentally damaging agrochemicals. Sorghum, a climate-resilient cereal, harbours a diverse microbiome that contributes significantly to its remarkable adaptability under adverse conditions. Harnessing the sorghum-associated microbiome, therefore, represents a promising, low-cost, and sustainable strategy to enhance sorghum productivity and resilience in smallholder farming systems. However, despite its potential, the adoption of microbiome-based technologies among smallholders remains limited due to a lack of local production units, poor government policies, knowledge gaps, and perceived risks. Although many studies report positive outcomes from microbiome-based interventions, translating this potential from controlled experiments to real-world field applications requires a critical evaluation of the efficacy, practicality, and limitations of microbial interventions. Furthermore, the outcomes of these studies are uneven, highly context-dependent, and often restricted to short-term or small-scale trials. This review, therefore, seeks to highlight current understanding of the sorghum microbiome, including its composition and the procedures for isolating and characterising beneficial microbes. It further evaluates the key challenges hindering adoption and proposes strategies to overcome them. Ultimately, this review advocates for integrating sorghum-associated microbiome technologies within integrated farming systems, underscoring their potential to enhance sustainable crop production, strengthen smallholder resilience, and contribute to the global sustainable development goals. Full article

While agriculture is experiencing localized crises, its indispensable role as the foundation of humanity’s food supply requires its uninterrupted functioning. This conventional system is therefore in a state of competition with alternative models, particularly agroecology, which offers a different paradigm for food production. Given this situation and the need to gather reliable information on regenerative agriculture (RA), this article provides a literature review on its principles, objectives, and edaphic benefits. Additionally, it presents a case study that offers practical knowledge of the techniques and actions implemented by an agroforestry farm in central Spain. With this goal, this article addresses key aspects of RA, such as the use of cover crops, and the integration of livestock, emphasizing its role in improving soil quality and increasing biodiversity, among other benefits. After reviewing numerous scientific articles, and despite widespread interest in RA, there is no commonly accepted definition, so there is a wide range of ways to define RA. Until a generalized definition is accepted, we advocate making proposals and implementing methods with extreme caution and based on the regional or local context in which it is defined. In this sense, based on the implementation of RA at the Kerbest Foundation farm, we propose regenerative agriculture as a set of agroecological actions and processes that fundamentally provide functional soil quality, food quality, ecosystem services, and, especially, healthy and economically profitable livestock farming. Based on all of the above, we can argue that RA is no longer merely a commitment made by farmers but, rather, an environmentally, economically, and socially sustainable solution grounded in scientific knowledge and technical experience. Full article

In the context of increasing climate variability and flood risk, this study explores how long-standing agricultural practices in the Hunter Valley, New South Wales, Australia, have fostered flood resilience through the integration of local agro-environmental knowledge and geomorphologic conditions. Employing a morpho-typological framework, the research identifies three flood adaptation landscape types (FALTs)—rolling hills, foot slopes, and flood plains—each reflecting distinct interactions between landform, soil, biodiversity, hydrology, and viticultural management. Through geospatial analysis, field surveys, and interviews with local farmers, the study reveals how adaptive strategies—ranging from flood avoidance to attenuation and acceptance—have evolved in response to site-specific hydrological and ecologic dynamics. These strategies demonstrate a form of ‘sponge landscape’ design, where agricultural systems are co-shaped with natural processes to enhance systemic resilience and long-term productivity. The findings underscore the value of preserving biocultural legacies and suggest that spatially explicit, context-based approaches to flood adaptation can inform sustainable landscape planning and climate resilience strategies in other rural regions. The FALT framework offers a replicable methodology for identifying flood adaptation patterns across diverse agricultural systems in Australia, supporting proactive land use planning and nature-based solutions. This research contributes to the discourse on climate adaptation by bridging traditional environmental knowledge with contemporary planning frameworks, offering practical insights for policy, landscape management, and rural development. Full article

Blow flies (Diptera: Calliphoridae) play a crucial role in the decomposition process and serve as important forensic indicators due to their predictable colonization patterns. This review focuses on the dynamics of maggot masses, highlighting their ecological roles, thermoregulation, and implications for forensics. We summarize data on the self-organizing behavior of maggot masses, which is influenced by chemical cues and environmental factors. These masses can generate internal temperatures that exceed ambient levels by 10–20 °C, accelerating larval growth and impacting competition among individuals. This localized heating complicates the estimation of the postmortem interval (PMI), as traditional models may not take these thermal influences into account. Furthermore, maggot masses contribute significantly to nutrient cycling and soil enrichment, while the behavior of the larvae includes both cooperation and competition, which is influenced by the species composition present. This review highlights challenges in PMI estimation due to heat production but also discusses advancements in molecular tools and thermal modeling that enhance accuracy. Ultimately, we identify knowledge gaps regarding species diversity, microbial interactions, and environmental variability that impact mass dynamics, suggesting future research avenues that could enhance ecological understanding and forensic applications. Full article