Climate change and depopulation confirmed as the main concerns affecting mountain areas in Europe

A study carried out by the UCO engaged some 500 local agents from 23 mountain regions to identify vulnerabilities and propose strategies to minimize them. In Andalucía, drought, pests and population loss were identified

University of Córdoba

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Researchers María del Mar Delgado and Pablo González, from the University of Cordoba

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Credit: University of Cordoba

In Europe, mountain areas account for about 36% of the total territory and are home to 16% of the population, but they are crucial for the continent as a whole. The availability of basic commodities, like water,depends on them, they play a key role in carbon sequestration, and are areas of biodiversity. And,as if that were not enough, the value chains of different consumer products, especially foodstuffs, are developed in these mountain systems. Therefore, they are complex and valuable socio-ecological systems that are threatened by diverse factors divergingfrom those affecting other areas. To shed light on these specificities and devise adaptation strategies to address them, a team atthe University of Cordoba turned to those who know thesemountain areas best: the local actors who live and work in them.

As part of the MOVING project, coordinated by the University of Cordoba, theteam analyzed the vulnerability of 23 mountainous areas in 16 European countries using a participatory methodology that engages the communities in each area related to the value chains of their most representative products. "In each territory, a multi-stakeholder platform was created in which the agricultural and livestock sector, research center staff, political representatives, companies, associations and institutions were invited to participate," explainedMaría del Mar Delgado, a professor in the Department of Agricultural Economics, Finance and Accounting, who is heading up the project. Through surveys, workshops and interviews with more than 500 local agents, the team managed to learn about their perceptions of the vulnerabilities of each area and the impact of these stresses and threats on the final product.

The results of this work, pioneering at the European level due to the scope of the territory analyzed, indicate that, although the elements that affect these spaces vary according to the characteristics of each one, changes in climate and demography are having the greatest impact on mountain areas. As explained by researcher Pablo González, with the Department of Forestry Engineering at the UCO, "aspects such as the lack of rainfall, or extreme weather events, as well as the depopulation of rural areas and changes in land use, are significantly affecting the value chains of products from mountain areas."The study shows that the mountain areas of Turkey and Bulgaria, Sierra Morena, and certain mountain ranges in Portugal,are detecting the greatest impact.

In the case of Andalucía, the project focused on theSierra Morena and its value chain of Iberian products,and the Sierras Béticas,with itsolive oil proceding mountain olive groves. In both cases, the most important element highlighted by local agents was drought or changes in precipitation patterns. In the case of theSierra Morena, pests, invasive species and the overexploitation of resources due to excessive livestock were also major concerns. As for mountain olive groves, another of the elements pointed out was population lossin rural areas.

More than 160 adaptation mechanisms

The good news, González explained, is that the impact of these changes can be reduced by incorporating adaptation mechanisms. In the course of the participatory process promoted by the MOVING project, local agents identified more than 160 adaptation mechanisms of different kinds, ranging from the implementation of sustainable agricultural and livestock practices, to commitments to applied research, to the development of effective policies, especially on the part of the European Union. "When we incorporate this adaptive capacity, the impact is reduced by more than half," explainedGonzález, who pointed out that not all the mechanisms proposedare equally applicable. However, "those that we do consider feasible, some of which are already being applied, could reduce the overall vulnerability of the systems by up to 50%."

The team responsible for the project states that one of the most pertinentaspects of the participatory process carried out is the opportunity it offers local agents to meet, listen to each other, debate and exchange impressions and knowledge. In addition, the study gathers their perceptions and what they are willing to change, thus enabling communities and value chains to be involved in the design and implementation of strategies to help reduce the vulnerability of mountain areas and, thus, that of Europe as a whole.

 

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Journal

AMBIO

DOI

10.1007/s13280-025-02153-5 

Method of Research

Data/statistical analysis

Subject of Research

People

Article Title

Assessing the vulnerability of mountain value chains to environmental and social drivers in Europe: A land-use and stakeholder-based approach

 

How can efficient and eco-friendly weed control in farmland be achieved?

Higher Education Press

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Credit: Mohammad MEHDIZADEH1,2 , Duraid K. A. AL-TAEY3 , Anahita OMIDI4 , Aljanabi Hadi Yasir ABBOOD5 , Shavan ASKAR6 , Soxibjon TOPILDIYEV7 , Harikumar PALLATHADKA8 , Renas Rajab ASAAD9

In farmland, the “resource competition” between weeds and crops is unceasing. These seemingly ordinary plants not only compete with crops for water, light, and nutrients but can also carry pests and diseases, and even inhibit crop growth by releasing allelopathic substances. For a long time, manual weeding and the application of chemical herbicides have been the primary means for farmers to combat weeds——manual weeding is time-consuming and labor-intensive, often requiring several hours of work per acre, while chemical herbicides, though efficient, lead to soil pollution, increased weed resistance, and even threaten ecological safety due to overuse. How can we ensure effective weed control while reducing environmental burdens? This longstanding agricultural challenge is being quietly rewritten by a new technology: machine learning.

An international team from countries including Iran, Iraq, Uzbekistan, and India has co-authored a review paper published in the journal Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024564). The corresponding author is Dr. Mohammad MEHDIZADEH from University of Mohaghegh Ardabili. The article outlines the potential applications of machine learning technology in weed management and provides insights for addressing the aforementioned issues. In simple terms, machine learning acts like an “intelligent brain” for farmland——it can analyze vast amounts of agricultural data, automatically identify patterns, and make precise decisions, shifting weed control from a “broad net” approach to “precision strikes”.

One major pain point of traditional weeding is the inability to distinguish between crops and weeds: when spraying herbicides, both crops and weeds are often covered, wasting chemicals and potentially harming crops. Machine learning’s “keen eye” can solve this problem. The article points out that by training computers to recognize the image features of weeds (such as leaf shape, color, and texture), algorithms can quickly differentiate between various weed species and even accurately locate weeds within dense crop growth.

More critically, machine learning can “calculate” the optimal weeding strategy. In the past, farmers often relied on experience when applying herbicides, resulting in either excessive use leading to waste or insufficient application resulting in incomplete weed control. Now, algorithms can comprehensively analyze historical application data, weed growth patterns, soil moisture, temperature, and other factors to predict weed growth trends in different areas and dynamically adjust the quantity and timing of herbicide application. This “on-demand application” model not only reduces farmers’ cultivation costs but also significantly alleviates the chemical burden on soil and water sources.

Additionally, machine learning can enable “real-time monitoring” of weeds. By continuously collecting farmland data through drones, sensors, and other devices, algorithms can track the spread of weeds in real-time. If an area shows a sudden increase in weed density, farmers will receive immediate warnings to prevent weeds from “growing rampantly”. This dynamic monitoring capability transforms weed control from “passive response” to “active defense”, particularly effective against invasive and rapidly spreading harmful weeds.

However, this technology is still in the research and validation stage. To be practically applied, challenges such as comprehensive data collection and the adaptability of algorithms in complex agricultural environments need to be addressed.

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Journal

Frontiers of Agricultural Science and Engineering

DOI

10.15302/J-FASE-2024564 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Advancing agriculture with machine learning: a new frontier in weed management

How to achieve intelligent and precise pesticide application in sustainable agriculture?

Higher Education Press

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Credit: Rohit ANAND1 , Roaf Ahmad PARRAY1 , Indra MANI2 , Tapan Kumar KHURA1 , Harilal KUSHWAHA3 , Brij Bihari SHARMA4 , Susheel SARKAR5 , Samarth GODARA6 , Shideh MOJERLOU7 , Hasan MIRZAKHANINAFCHI8

In traditional agriculture, crop disease control often relies on the extensive spraying of chemical pesticides. However, this “one-size-fits-all” approach not only wastes resources but also risks environmental pollution and threats to human health. With global climate change and increasing pathogen resistance, reducing pesticide dependence through smarter, more precise methods while ensuring crop yields has become a major challenge for sustainable agriculture. Is there a technology that can rapidly identify diseases and apply pesticides precisely, achieving “targeted treatment”?

Dr. Roaf Ahmad Parray from ICAR-indian agricultural research institute (ICAR-IARI) and his colleagues provide an answer in a study published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024572). In this research, an international team of scientists from India, Denmark, and the United States developed an innovative technology integrating spectral sensors, machine learning models, and an intelligent spraying system, successfully applying it to control black rot disease in cauliflower. This technology, comprising three core components—non-destructive detection, intelligent decision-making, and targeted pesticide application—significantly reduces pesticide use and offers new insights for green agriculture.

Traditional disease detection relies on manual observation, which is time-consuming, labor-intensive, and error-prone. The researchers adopted a novel approach, using spectral sensors to capture reflected light signals from plant leaves. Healthy leaves and those infected with black rot exhibit distinct differences in light reflection, particularly in visible and near-infrared bands. By analyzing these “spectral fingerprints”, the sensors rapidly assess plant health. However, how can disease-infected areas be accurately distinguished from healthy ones within vast spectral data? To address this, the researchers introduced machine learning algorithms, comparing decision trees and support vector machines (SVM). Results showed that the SVM model outperformed the decision tree, achieving a test accuracy of 96.7% versus 89.9%. This high-precision model was embedded into the intelligent spraying system’s control unit, serving as the “brain” to determine pesticide application.

Traditional spraying equipment often covers the entire farmland, while the intelligent spraying system in this study is as precise as a “surgeon”. When the sensor detects a diseased area, the system sprays pesticides only on the diseased part through a micro pump and a special nozzle; if a plant is identified as healthy, the spraying function is automatically turned off. Field trials have shown that this system successfully identified and treated 75% of the diseased plants in a 100-square-meter cauliflower test field, and avoided mis-spraying 87.5% of the healthy plants. Compared with traditional backpack sprayers, the intelligent system reduced the amount of pesticides used by 72.5% and saved 21% of the spraying time.

At the same time, the equipment uses low-cost materials and open-source hardware to ensure that small-scale farmers can afford it. The distance between the sensor and the spraying unit of the equipment has been optimized and can work stably within the range of 25–45 centimeters, adapting to farmlands with different planting densities. In addition, the system only needs to regularly calibrate the whiteboard reference value, and it is simple to operate, making it suitable for farmers lacking professional skills.

Currently, the researchers have completed the preliminary verification in the experimental field of the Indian Agricultural Research Institute. Future research will explore the applicability of this technology to other crops (such as tomatoes, potatoes) and diseases (such as downy mildew), and expand its application by combining with drone technology.

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Journal

Frontiers of Agricultural Science and Engineering

DOI

10.15302/J-FASE-2024572 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology

 

Machine vision + deep learning: how to achieve fast and accurate fruit grading?

Higher Education Press

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Credit: Amna1 , Muhammad Waqar AKRAM1 , Guiqiang LI2 , Muhammad Zuhaib AKRAM3 , Muhammad FAHEEM1 , Muhammad Mubashar OMAR4 , Muhammad Ghulman HASSAN1

In the context of a continually growing global population and rising food demand, fruit, as an important source of nutrition, requires quality grading and efficient processing, which are critical links in the agricultural supply chain. Fruit grading refers to the classification of fruits based on indicators such as external defects and ripeness, directly influencing their market value and food safety. However, traditional grading has long relied on manual visual assessment, which is not only time-consuming and labor-intensive but also prone to high error rates, making it difficult to meet large-scale processing demands. So, how can more intelligent technologies replace manual labor to achieve automated and precise fruit grading?

Recently, Dr. Muhammad Waqar Akram and his team from the Department of Farm Machinery and Power at University of Agriculture Faisalabad in Pakistan developed a “Machine Vision-Based Automatic Fruit Grading System”, offering a new solution. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2023532).

This system deeply integrates “machine vision” and “deep learning”, constructing a fully automated process from defect detection to mechanical sorting. In simple terms, it operates like taking “photos” of fruits, analyzing the details in the images to assess quality, and directing a robotic arm to sort the fruits into different graded boxes. Specifically, the system comprises two key components: a defect detection module and a mechanical sorting module.

Defect detection is the first step in grading. The research employs a “dual-track technical approach”: on one hand, traditional image processing algorithms are used to calculate the proportion of defects on the fruit’s surface through steps such as image preprocessing, threshold segmentation, and morphological operations; on the other hand, convolutional neural networks (CNNs), which excel in image recognition, are introduced to train on image data of mangoes and tomatoes. The dataset used for training the CNN includes both publicly available fruit images and actual captured samples, covering various states from fresh to rotten, ensuring the model can adapt to the complex variations of real-world scenarios. Experiments show that the traditional image processing algorithm achieves detection accuracies of 89% and 92% for mangoes and tomatoes, respectively, while the CNN model demonstrates even higher validation accuracies of 95% and 93.5%, enhancing the reliability of defect identification.

Once the detection results are confirmed, the system sends commands to the mechanical sorting module via a microcontroller (Arduino Uno). This module consists of a conveyor belt and a servo motor-driven sorting arm: as the fruit moves along the conveyor belt, a camera captures images and analyzes them in a designated area. If defects are detected, the sorting arm accurately acts to place the problematic fruit into the corresponding grading box. The entire process is interconnected, taking only a few seconds from image capture to sorting completion, and the hardware costs are relatively low, making it suitable for farms or small processing plants.

It is noteworthy that this system not only addresses the efficiency and error issues of manual grading but also showcases the complementary advantages of “traditional algorithms + deep learning” through technological integration: traditional image processing is fast and cost-effective, making it suitable for scenarios with high real-time requirements; deep learning, on the other hand, can capture more subtle defect features, improving accuracy in complex situations. The research found that even when faced with challenges such as significant color variations on mango skins and complex surface textures on tomatoes, the system remained stable, providing a generalized solution for grading various fruit types.

Currently, the system has demonstrated good practicality in the grading of mangoes and tomatoes. In the future, further optimization of hardware design (such as adding multi-angle cameras) and expanding the range of applicable fruit types could explore its integration with more agricultural scenarios.

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Journal

Frontiers of Agricultural Science and Engineering

DOI

10.15302/J-FASE-2023532 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Machine vision-based automatic fruit quality detection and grading

Article Publication Date

6-May-2025

 

Can straw mulching affect soil CO2 emissions in bamboo forests?

Higher Education Press

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Credit: Quan LI1,‡ , Jiarui FU1,‡ , Jiahui ZENG1 , Chao ZHANG1 , Changhui PENG2,3 , Lei DENG4 , Tingting CAO1 , Man SHI1 , Zhikang WANG1 , Junbo ZHANG1 , Weifeng ZHANG5 , Yi ZHANG5 , Xinzhang SONG1

In the bamboo forests of southern China, mulching the ground with straw during winter is a common management practice. Farmers use materials such as rice straw and bamboo branches to insulate the soil, helping to retain warmth and moisture while also promoting the early harvest of bamboo shoots, thereby increasing economic returns. However, this seemingly “routine” operation may quietly alter the carbon exchange between the soil and the atmosphere. Previous studies have predominantly focused on the short-term effects of straw mulching on soil CO2 emissions, mainly targeting agricultural ecosystems. However, does straw mulching lead to changes in soil carbon emissions in artificial forests in humid regions?

Professor Xinzhang Song (Zhejiang A&F University) et al. conducted a study that revealed the response of soil CO2 emissions in bamboo forests of humid regions to straw mulching and its long-term effects. The research found that straw mulching not only significantly increased soil carbon emissions in the short term but also had enduring effects that persisted for at least three years after the removal of the mulching material. The study has been published in the journal Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025607).

The researchers established three treatment groups in the bamboo forest in Huzhou, Zhejiang: “no mulch”, “one-year mulch”, and “continuous three-year mulch”, and continuously monitored soil CO2 emissions and related indicators during the mulching period and the “enduring effect period” after the removal of the mulching materials. The results indicated that during the mulching period, soil CO2 emissions in the straw-mulched areas were approximately 18 times higher than those in the unmulched areas. This was primarily because the mulching material acted like a “thermal blanket”, raising soil temperatures and creating a more favorable environment for microbial activity and the growth of bamboo rhizomes and shoots, thus accelerating the decomposition of soil organic matter and root respiration. Notably, even during the “enduring effect period” after the removal of the mulching material, carbon emissions were still 230%–270% higher than in the uncovered areas. At this stage, the impact of soil temperature on emissions weakened, while soil nutrients became the key driving factor. During the mulching period, the decomposition of straw and accompanying materials such as pig manure significantly increased the levels of organic carbon, nitrogen, and phosphorus in the soil, providing a richer “food” source for microbes and continuously stimulating their activity, thus maintaining elevated CO2 emission levels.

This finding breaks through previous limitations that focused only on the effects during the mulching period, confirming for the first time that straw mulching has a lasting impact on soil CO2 emissions in artificial forests in humid areas. The study also found that while mulching increases soil CO2 emissions, the organic carbon content in the soil of the mulched areas significantly increased by 27%–72%, indicating that straw mulching not only promotes carbon release but also enhances carbon storage capacity by increasing soil carbon input, providing new insights for carbon management in artificial forests in humid regions.

Moreover, there was no significant difference in the enhancement of soil CO2 emissions between the one-year cover and the continuous three-year cover, suggesting that short-term mulching can have lasting effects. Based on this, the authors recommend that in practical production, the thickness and quantity of mulching materials can be appropriately reduced to ensure bamboo shoot yields while lowering CO2 emission intensity, achieving a “win-win” for economic returns and carbon sink capacity.

This study constructed a multidimensional evidence chain demonstrating the impact of mulching measures on carbon emissions by monitoring soil microbial biomass, functional genes, and environmental factors. It not only fills the research gap on carbon cycling in bamboo forests in humid regions but also provides theoretical references for the management of other forest ecosystems through its revelation of nutrient-driven enduring effect mechanisms.

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Journal

Frontiers of Agricultural Science and Engineering

DOI

10.15302/J-FASE-2025607 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Straw mulching has an enduring positive effect on soil CO2 emissions in a humid plantation