Armored scale species (Hemiptera: Coccomorpha: Diaspididae) associated with fruit trees in the state of Guerrero, Mexico, were identified through sampling conducted during 2014 and 2015 in both commercial and backyard orchards. Adult females were slide-mounted in Canada balsam and identified to species level using dichotomous keys. A total of 13 species were recorded, belonging to three tribes and 13 genera, including two new records for Mexico and 13 new records for Guerrero. Of the 23 fruit tree species sampled, armored scales were detected on 14 species from 10 plant families. Tamarindus indica L. (Fabaceae) is reported for the first time as a host of two armored scale species in Mexico. Forty-six percent of the specimens were found in backyard orchards, 15 % in commercial orchards, and the remainder in both systems. Citrus trees hosted the greatest diversity of diaspidids, and Aspidiotus destructor Signoret was the most polyphagous species, associated with eight fruit tree species. Most armored scale species collected infested more than one plant organ (leaves, shoots, stems, and fruits) on the same tree (eight species, 62 %), whereas three species (23 %) were observed exclusively on leaves. Diaspididae species occurred from February to August and from sea level (0 m) up to an altitude of 1750 m, with most concentrated in tropical zones (Costa Grande and Costa Chica regions) at altitudes below 1000 m. The white mango scale (A. tubercularis Newstead) was the only species present year-round on mango trees in both commercial and backyard orchards. These findings expand the knowledge of armored scale diversity and distribution in Mexico.
This article aims to analyze ecological economics as a complex adaptive system by integrating principles of evolutionary biology, with the goal of understanding the dynamics of coevolution between socioeconomic and ecological systems under biophysical constraints. A qualitative, theoretical-explanatory methodology is used, based on transdisciplinary literature review and abductive reasoning, drawing on a corpus of specialized scientific literature. The results indicate that economic systems operate through evolutionary mechanisms of variation, selection, and retention, conditioned by nonlinear dynamics, thermodynamic limits, and multiscale interactions. It is concluded that sustainability must be understood as a dynamic co-evolutionary process, which implies moving beyond linear approaches and adopting analytical frameworks grounded in complexity, resilience, and institutional diversity.
This work aimed to determine the content of potentially toxic elements in agricultural soils in the Pozarevac and Topola municipalities to provide information for sustainable soil management in central Serbia. Soil was sampled at 106 sites of plowland and 59 sites of other agricultural land. The content of Cr, As, and Cd was significantly greater in Topola municipality compared to Pozarevac municipality, and the opposite was true for the Hg content. In addition, the Hg and Ni content was significantly greater for plowland (Hg = 0.81 mg kg-1 and Ni = 85.6 mg kg-1) compared to other agricultural land in the Pozarevac municipality (Hg = 0.68 mg kg-1 and Ni = 46.87 mg kg-1). The Ni and Pb content of agricultural land in both municipalities, the Cr and Cd content in Topola municipality, and the Hg content in Pozarevac municipality were between the maximum regulatory limit values and remediation values of these potentially toxic elements. These results could be useful indicators of the quality of agricultural land in central Serbia.
Although guides and manuals exist for the production of the Malaysian prawn (Macrobrachium rosenbergii de Man, 1879), the lack of specific knowledge regarding the maintenance and reproduction of this species in Mexico has limited its development. The objective of this study was to evaluate reproductive performance (RP), gonadal maturation (GM), and embryonic development (ED) under different stocking densities and sex ratios in Malaysian prawn broodstock. The experiment lasted 45 d, and three sex ratios were evaluated: one female and one male (1F:1M), two females and one male (2F:1M), and three females and one male (3F:1M) in 1 m3 ponds filled to 500 L. The number of molts, the number of ovigerous females (OF), the frequency of fertilization, the hatching rate (RP) (by direct observation), and the duration of the developmental stage (DS) and the gonadal maturation (GM) were analyzed using Fisher’s exact test for two ratios (α = 0.05) and one-way analysis of variance (α = 0.05). The proportion of OF and hatchings was similar across the different sex ratios. The number of recurrent OF was higher in the 1F:1M and 2F:1M ratios, and two complete reproductive cycles were completed in 45 d. In the 3F:1M ratio, the RP was group-based. In the 1F:1M ratio, each shrimp remained at opposite ends, and under this ratio, the male exhibited less protective activity. The duration of the reproductive cycle differed, being 16 d in the 2F:1M and 3F:1M ratios and 18 d for the 1F:1M ratio. In the 3F:1M ratio, the male’s protective behavior toward ovigerous females was more intense and prolonged, which may have influenced the shorter ED. These results provide evidence for the reproductive management of the Malaysian prawn at different densities and sex ratios. These findings strengthen the technical basis for optimizing the management and culture strategies of M. rosenbergii broodstock in Mexico.
The tortilla is a staple food in the Mexican diet. However, in recent years, it has been declining in quality and nutritional value due to the use of flour and the industrialization of production processes. Fresh traditional tortillas are known to possess superior nutritional and nutraceutical properties compared to commercially produced tortillas, along with unmatched flavor and texture. Consumers have increased their preferences for local, more natural products produced in an eco-friendly agricultural manner. Thus, soil degradation, water pollution, and the loss of biodiversity are avoided. However, the price consumers are willing to pay for a tortilla with these attributes is unknown. Using the contingent valuation method (CVM) in its referendum and double-bounded formats, the aim of this investigation was to estimate consumers’ willingness to pay (WTP) for the tortilla they consume with the following attributes: native maize content, organic production, and traditional nixtamalization. Moreover, the variables that explained the behavior of the WTP were determined. A total of 216 surveys were conducted between January and March 2024 in 15 municipalities of the State of Mexico belonging to the metropolitan area of the Valley of Mexico. The double-bounded CVM displayed the highest theoretical consistency. The variables of price, monetary income, gender, education level, economic dependents, and age of the respondent helped estimate the WTP. The estimated value was MXN 36.12 per kg of tortilla.
Raspberry pickers are a specific group of agricultural workers whose labor conditions have received limited attention in research. Their working conditions, the health problems they face, and the factors affecting their performance remain scarcely explored. The aim of this pilot study was to analyze the relationship between farmworkers’ self-perceived health status and their work performance, measured by the number of buckets harvested and the income derived from piece-rate work. Data were collected through a daily self-evaluation survey administered to a group of pickers over 53 harvest days. Workers’ perceived health status was recorded at the beginning, during, and at the end of each workday, along with the number of buckets harvested per day. Simple correlation analysis yielded an r coefficient of 0.66, corresponding to a determination coefficient (R2) of 0.44, indicating a moderate relationship between self-perceived health status and work performance. The results showed that poorer self-perceived health status was associated with a 21 % reduction in the number of buckets harvested relative to the group average. This reduction led to income differences of up to 35 % between the most and least productive pickers. These findings highlight the impact of health on both productivity and income among these workers. For future research, studies with larger samples and models incorporating additional factors are recommended to further clarify the relationship between health and productivity in this type of work.
Due to their broad productivity and nutritional value, legumes are used in animal production systems. The aim of this study was to evaluate the productive behavior and the chemical quality of different varieties of forage soybean (Glycine max (L.) Merr.) using a growth analysis in the dry tropics of Mexico. The treatments included the following evaluated varieties: Salcer, Ojo de Tigre, Valente, and Albina. The variables determined were total dry matter (TDM), yield by component, plant height, intercepted radiation, plants per square meter, weight per stem, crude protein, acid detergent fiber, and neutral detergent fiber. The variety with the highest TDM yield was Ojo de Tigre, with 5562 kg ha-1, whereas Salcer had the lowest, with 4626 kg ha-1 (p < 0.05). The intercepted radiation increased with the age of the plant in all four varieties until day 61 after harvest, reaching an average of 96 %, a value that remained until day 68. The genotype with the best attributes in dry matter yields, structural characteristics, and plant height was Ojo de Tigre. The optimum moment for harvest was established at day 61, when an average of 95 % intercepted radiation and better quality were obtained, which defines the optimum cutting point for the conditions of the dry tropics of Mexico.
The present investigation describes an advanced multi-task deep learning framework for automated inspection of coffee cherry quality using YOLOv8 with color-based segmentation and Vision Transformer-Convolutional Neural Network (ViT-CNN) feature extraction. The model performs ripeness stage classification, defect detection, and size and shape analysis. For ripeness detection, YOLOv8 was enhanced with a color segmentation module, achieving class-wise accuracies of 90–95 % for unripe, partially ripe, and fully ripe cherries, with moderate performance (85 %) for overripe samples. ViT-CNN feature maps improved segmentation clarity and bounding-box localization, particularly in high-density clusters. Defect detection was carried out across five categories (healthy, blackened, moldy, wrinkled, and insect-damaged), achieving F1-score values between 0.88 and 0.96 and mean average precision at 50 % intersection over union (mAP@50) values above 0.97 for key defect classes after 150 training epochs. Quantitative evaluation of morphological characteristics for size and shape assessment further demonstrated model robustness, with insect-damaged cherries reaching a contour accuracy of 0.98 and an Intersection over Union (IoU) of 0.96. Comparative analysis with YOLOv5 and Faster Region-Based Convolutional Neural Network (Faster R-CNN) showed superior performance of the proposed architecture across all metrics, including precision, recall, F1-score, and mAP. By incorporating contextual embeddings and attention mechanisms, the framework enables accurate, real-time sorting for smart agricultural systems.
Mezcal agave (Agave angustifolia Haw.) is a key resource for the economy and culture of the State of Mexico. However, it faces a phytosanitary crisis due to pests such as the agave weevil (Scyphophorus acupunctatus Gyllenhaal, 1838) and diseases such as Fusarium oxysporum wilt, causing losses of up to 50 % in production. The lack of efficient monitoring systems justifies the development of a Geographic Information System (GIS) to optimize phytosanitary management. This study aimed to design a GIS that integrates biophysical and management variables to identify risk zones and facilitate integrated management strategies. Four plots were monitored in the municipalities of Malinalco and Zumpahuacán in 2024, with 100 plants per plot georeferenced. The incidence of S. acupunctatus and F. oxysporum was assessed monthly, along with environmental and management variables. Data were processed using QGIS 3.24, generating risk maps through interpolation (Inverse Distance Weighted (IDW) and Kriging) and spatial correlation analysis (Moran’s I). Statistical analyses included analysis of variance (ANOVA) and multiple regression. The results demonstrated that GIS-generated risk maps allow highly accurate identification of infestation hotspots. In Malinalco Centro, weevil incidence was positively correlated with accumulated precipitation (r = 0.65, p < 0.05) and clay soils, exhibiting a spatial aggregation pattern (Moran’s I = 0.42, p < 0.01). For F. oxysporum, soil moisture (>60 %) was the most influential factor (β = 0.62; p = 0.002), with critical zones expanding radially at 15 m per month. The Random Forest model predicted weevil incidence with 88.2 % accuracy (AUC-ROC = 0.91). This integrated approach, replicable in other agave-growing regions, would contribute to crop sustainability by enabling spatially targeted interventions, optimizing resources, and reducing input use.
The Molino de Flores Nezahualcóyotl National Park has a high ecological and cultural relevance in the periurban area of Texcoco, Mexico. The aim of this study was to evaluate the environmental pressures, the state of the ecosystem, and the effects of management activities through the use of the Pressure-State-Response (PSR) model. The proposed hypothesis suggests that anthropogenic pressures negatively affect the health of the forest, while restoration efforts are designed to alleviate these impacts. The information was obtained by interviewing authorities, making field observations, and conducting perception surveys on visitors. The results identified intensive tourism, invasive exotic species, and forest fires as the main sources of pressure. The state of the ecosystem reflects a high resilience and a biological wealth of 540 registered species, with evidence of the recovery of native fauna and flora after management interventions. Institutional responses emphasize the implementation of mycorrhizal reforestation, fire management strategies, and environmental education initiatives. In conclusion, the PSR model is an effective tool for an integrated diagnosis. Although the system is resilient, it is imperative to strengthen funding and public awareness to ensure the sustainability of the area against urban pressure.
Smallholder farmers cultivating limited landholdings often experience low yields and reduced returns, as restricted plot sizes limit crop rotation and expansion. Vertical farming provides a practical alternative by enabling intensive production in compact and densely populated areas, supporting year-round cultivation and increased food output. Artificial Intelligence (AI) and the Internet of Things (IoT) play a central role in modern agriculture by enabling precision farming and data-driven decision-making. The proposed system integrated soil-based, hydroponic, and aeroponic techniques within a unified vertical framework. Advanced irrigation and monitoring technologies, including moisture sensors and image-based crop analysis, optimized water usage, nutrient delivery, and crop health management. The approach automated irrigation and nutrient control and achieved a disease detection accuracy of 96 %, demonstrating improved performance compared to conventional machine learning models. Real-time monitoring through sensors and imaging devices reduced manual intervention, improved resource utilization, and supported sustainable agricultural practices. Overall, the system enhanced productivity across diverse farming conditions while promoting resource efficiency, sustainability, and climate resilience.
The study aimed to develop a Rhizopus oligosporus starter culture for using it in the preparation of amaranth-based and soy-based tempeh and to compare their resulting sensory profiles using the Rate-All-That-Apply (RATA) technique. In this study, the Rhizopus oligosporus inoculum for tempeh preparation was produced by Solid State Fermentation in rice. During inoculum production, the effects of initial pH (4.5, 5.0, 5.5, 6.0, and 6.5) and initial temperature (25, 30, 35, and 40 °C) were determined. Products generated during fungal metabolism were analyzed: organic acid content by titration, lactic acid production by spectroscopy, and the final pH of the medium. Once the appropriate conditions for inoculum production were determined, a comparative analysis of the sensory profiles of amaranth-based and soy-based tempeh was performed using the Rate That Apply (RATA) technique. The results showed that the highest lactic acid production was obtained when the initial pH of the medium was adjusted to pH 5.5 (1.75 g L–1) and at 40 °C (1.85 g L–1). Likewise, the highest organic acid production was observed at an initial pH of 5.0 (0.066 g L–1), pH 6.0 (0.105 g L–1), and at 35 °C (0.69 g L–1). Different sensory profiles were observed. The soy-based tempeh differed from the amaranth-based tempeh in its fishy odor and flavor. Therefore, the lactic acid content, organic acids, and final pH of the medium are influenced by the initial pH and temperature of the medium. The sensory profile of tempeh is influenced by the raw materials used in its preparation.
Intensive agricultural management causes soil alteration, which leads to a decrease in some of its physical characteristics and a change in its quality and fertility. Bulk density and hydraulic conductivity are parameters used to evaluate these effects on food production. Other factors, such as soil organic matter, are also relevant for assessing soil quality, including physical quality, due to their close relationship with other soil properties. The objective of this study was to evaluate the physical quality of soils subjected to frequent changes resulting from intensive agricultural management. Five soils (S1, S2, S3, S4, and S5) classified as Vertisols from Ac & aacute;mbaro, in the state of Guanajuato, Mexico, were evaluated. Sampling was carried out at a depth of 0 to 20 cm to obtain a homogeneous composite sample. Nineteen physical variables and the soil organic matter content were determined. A principal component analysis was applied to rank the most important variables and calculate physical quality indices. Soil S4 had a low degradation index, associated with particle stability and high hydraulic conductivity (7.0 cm h-1). Soils S1, S2, S3, and S5 were classified as having a moderate degradation index, attributable to intense mechanical effects such as subsoiling and harrowing. When the organic matter variable was integrated into the generation of the indices, the physical quality of all soils was considered high, and the degradation index decreased as a result of the high organic matter content (5.85-8.58 %) and reserves, which favor adequate physical conditions. The addition of labile organic materials, such as manure and compost, in intensive agriculture improves the physical quality of the soil over prolonged periods, with positive effects on its conservation.
Goat (Capra hircus Linnaeus, 1758) production is a key livelihood activity in Zacatecas, Mexico, but it faces increasing environmental, social, and economic pressures typical of dryland systems. This study offers an integrative narrative review with a systematic search and transparent selection criteria to synthesize evidence on the sustainability of goat production systems in Zacatecas. Peer-reviewed literature and technical documents were analyzed alongside official contextual indicators. The synthesis shows a decline in the caprine sector over recent census periods, with a producer profile marked by demographic vulnerability. Across the evidence, sustainability challenges stem from interconnected pathways where drought and rainfall variability decrease forage and water availability, worsen seasonal feed shortages, and boost reliance on purchased inputs, raising costs and increasing land degradation risks. These environmental pressures interact with herd management issues such as feeding, preventive health, parasite control, and reproduction, while limited access to coordinated services, infrastructure, and stable markets hampers value capture and reinvestment. Significant evidence gaps remain for Zacatecas-specific, outcome-based assessments (profitability during drought, rangeland condition metrics, health burdens, and value-chain performance), indicating the need for ongoing monitoring. Key leverage points include drought preparedness and feed planning, improving water access, preventive herd health, strengthening producer organizations and extension services, and developing feasible value-added options for dairy and meat products.
Stevia is an herb used as a raw material to manufacture a low-calorie natural sweetener. Stevia plants are generally propagated by stem cuttings. Nonetheless, this method is not sufficient to meet global demand. The aim of this study was to determine the effect of applying an arbuscular mycorrhizal fungus (AMF) consortium and indole-3-butyric acid (IBA), in both powder and solution, on the greenhouse propagation of stevia stem cuttings. Applying AMF and IBA promoted greater growth across the different parameters assessed in stevia stem cuttings, increasing mycorrhizal colonization, particularly arbuscule content in roots. Therefore, the use of AMF and IBA should allow the production of stevia stem cuttings with greater vigor in a shorter period of time, reducing crop production costs by optimizing the dosage and methods of plant hormone application.
Ecological circular agriculture is a necessary choice for sustainable agricultural development. It is crucial for alleviating resource-environmental pressures and balancing ecological and economic progress. Within this context, the role of carbon trading policy is significant, as it acts as a catalyst in promoting the vitality of ecological circular agriculture. However, there is a dearth of research on how carbon trading affects the development of ecological circular agriculture. Therefore, this study aims to fill this gap by examining the impact of carbon trading policy on ecological circular agriculture and its underlying mechanisms. Panel data from 30 provincial-level administrative regions in China spanning 2006-2021 was used to construct a multidimensional index for ecological circular agriculture and apply a difference-in-differences (DID) approach. The findings reveal that carbon trading policy can enhance ecological circular agriculture in pilot provinces (municipalities) by 3.5 %, primarily through improved ecological technology and agricultural carbon productivity to drive the green transformation of agriculture. The effects are most pronounced in the western Chinese region and areas with stronger agricultural labor productivity. This research improves the understanding of carbon trading mechanisms in agricultural systems and provides insights for designing effective carbon trading mechanisms.
In the mid-20th century, the theory of economic growth predicted a gradual decline in the agricultural sector’s share of the overall economy. This prediction is based on specific assumptions about the sector that are not necessarily true at all times. In the case of Mexico, to test this theoretical prediction, an empirical analysis was conducted using quarterly data covering the period from 1993 to 2024. The hypothesis to be tested was that the contribution of agriculture may follow its own trajectory, independent of the prediction of a secular decline in the sector. During the period examined, using a linear adjustment of the agricultural sector’s contribution with a structural break, both a declining trend and a more recent increasing trend in its contribution were found, thus refuting the prediction of classical growth theory. Although the theoretical prediction points in one direction, the empirical result is different. The share of the agricultural sector may follow its own pattern in economic evolution. In the search for an explanation of the sector’s recent trend, quantity and price indices were constructed to explore whether the aforementioned adjustment is due to price or quantity. The results indicate that the price component is the basis for explaining the observed phenomenon.
Soil quality is essential for sustainable agriculture. Nonetheless, inadequate irrigation methods, improper fertilizer use, and over-cultivation reduce soil quality, thereby decreasing soil fertility. Precise soil quality prediction is crucial for improving agricultural practices. Conventional deep learning models often encounter problems related to superfluous features, high computational demands, and inaccurate predictions. In this work, an innovative deep learning architecture integrating optimal feature selection was proposed to address these challenges. Initially, the Adaptive Parrot Optimization (AdPo) method was used to identify the most relevant features from pre-processed soil data. The Extended Cross Stage Pyramid Network (ExCSP_Net) was introduced to improve soil quality prediction. This network integrates a gated recurrent unit (GRU)-based attention module into the main pathway of the Cross Stage Partial (CSP) model to capture long-range dependencies and emphasize relevant information. In addition, a stacked autoencoder was incorporated before the feature-sharing stage in the short path of the CSP model to reduce dimensionality and generate meaningful representations. The AdPo+ExCSP_ Net model demonstrated outstanding performance, achieving an accuracy of 98.58 %, recall of 98.09 %, precision of 98.32 %, F1-score of 98.15 %, Mean Absolute Error (MAE) of 0.53, Root Mean Square Error (RMSE) of 0.65, and coefficient of determination (R2) of 0.99. These findings highlight the effectiveness of the proposed methodology for accurate soil quality prediction and the promotion of sustainable agricultural practices.
The agricultural sector is vulnerable to flooding, as it causes damage to soil, crops, and hydro-agricultural infrastructure, thereby limiting production. Quantifying and delineating flood-prone areas is important, as food security is at stake. This study was conducted in Irrigation District (ID) 008 Metztitl & aacute;n, Mexico, which experiences recurrent flooding that affects the production system and hydro-agricultural infrastructure. The objective of this study was to develop and apply a methodology to assess flood risk in agricultural areas focused on maize cultivation. A basic hydrological and hydraulic model was developed for the proposed methodological framework for agricultural risk analysis, considering three flood factors that affect crops: A) duration, B) depth, C) velocity, and the phenological stage of growth. Based on these factors, parameters were proposed for assessing hazard, vulnerability, and exposure value to calculate risk in monetary terms using map algebra. The scenario analyzed was for a 20-year return period, determining that 94.7 % of the total area of the ID presents some degree of risk. The proposed methodology allowed for the generation of risk maps. Delineating risk zones can aid decision-making to mitigate flood damage in the agricultural sector.
The production of organic coffee (Coffea arabica L.) under shade contributes to mitigating climate change, as it generates lower greenhouse gas (GHG) emissions than conventional cultivation, thereby reducing its carbon footprint (CF). This study estimated the CF of coffee produced by the Comon Yaj Noptic SPR de RL cooperative in the municipality of La Concordia, Chiapas, Mexico, with the aim of identifying critical emission points and opportunities for environmental improvement. Information was collected from 161 plots through visits and interviews with producers, and wet milling data was integrated using emission factors from the Intergovernmental Panel on Climate Change (IPCC). The CF was estimated per kilogram of green coffee produced, considering emissions from plot management to the packaging of the final product. In the primary stage (plot management to parchment coffee), CF was 0.401 ± 0.079 kg CO2e, with variability associated with altitude, plantation age, and planting density. The main sources were pulp decomposition (0.262 kg) and wastewater (0.078 kg) due to methane and nitrous oxide emissions. During processing (roasting, grinding, and packaging), CF was 0.415 kg CO2e, with roasting being the main source (0.304 kg), followed by packaging (0.086 kg) and grinding (0.009 kg). The average CF for the entire production chain was 0.816 kg CO2e, with a range of 0.758–1.271 kg CO2e, showing consistency and low impact compared to conventional systems. The results confirm that shade-grown organic coffee has low CF and show high sustainability potential. However, opportunities for improvement were identified, such as the use of clean energy, efficient wastewater management, and the use of pulp as a by-product.
